
@inproceedings{lahat_multidimensional_2011,
	title = {Multidimensional {ICA} and its performance analysis applied to {CMB} observations},
	booktitle = {Acoustics, Speech and Signal Processing {(ICASSP)}, 2011 {IEEE} International Conference on},
	author = {Lahat, D. and Cardoso, {J.F.} and Le Jeune, M. and Messer, H.},
	year = {2011},
	pages = {3724--3727}
},

@article{lee_blind_1997,
	title = {Blind separation of delayed and convolved sources},
	journal = {Advances in neural information processing systems},
	author = {Lee, {T.W.} and Bell, {A.J.} and Lambert, {R.H.}},
	year = {1997},
	pages = {758--764}
},

@inproceedings{choi_local_2000,
	title = {Local stability analysis of flexible independent component analysis algorithm},
	volume = {6},
	booktitle = {Acoustics, Speech, and Signal Processing, 2000. {ICASSP'00.} Proceedings. 2000 {IEEE} International Conference on},
	author = {Choi, S. and Cichocki, A. and Amari, S.},
	year = {2000},
	pages = {3426--3429}
},

@article{funaro_independent_2003,
	title = {Independent component analysis for artefact separation in astrophysical images},
	volume = {16},
	number = {3-4},
	journal = {Neural networks},
	author = {Funaro, M. and Oja, E. and Valpola, H.},
	year = {2003},
	pages = {469--478}
},

@article{calhoun_method_2004,
	title = {A method for comparing group {fMRI} data using independent component analysis: application to visual, motor and visuomotor tasks},
	volume = {22},
	shorttitle = {A method for comparing group {fMRI} data using independent component analysis},
	number = {9},
	journal = {Magnetic resonance imaging},
	author = {Calhoun, {V.D.} and Adal\i{}, T. and Pekar, {J.J.}},
	year = {2004},
	pages = {1181--1191}
},

@article{mckeown_spatially_1998,
	title = {Spatially independent activity patterns in functional {MRI} data during the stroop color-naming task},
	volume = {95},
	issn = {0027-8424},
	url = {http://www.ncbi.nlm.nih.gov/pubmed/9448244},
	abstract = {A method is given for determining the time course and spatial extent of consistently and transiently task-related activations from other physiological and artifactual components that contribute to functional {MRI} {(fMRI)} recordings. Independent component analysis {(ICA)} was used to analyze two {fMRI} data sets from a subject performing 6-min trials composed of alternating 40-sec Stroop color-naming and control task blocks. Each component consisted of a fixed three-dimensional spatial distribution of brain voxel values (a "map") and an associated time course of activation. For each trial, the algorithm detected, without a priori knowledge of their spatial or temporal structure, one consistently task-related component activated during each Stroop task block, plus several transiently task-related components activated at the onset of one or two of the Stroop task blocks only. Activation patterns occurring during only part of the {fMRI} trial are not observed with other techniques, because their time courses cannot easily be known in advance. Other {ICA} components were related to physiological pulsations, head movements, or machine noise. By using higher-order statistics to specify stricter criteria for spatial independence between component maps, {ICA} produced improved estimates of the temporal and spatial extent of task-related activation in our data compared with principal component analysis {(PCA).} {ICA} appears to be a promising tool for exploratory analysis of {fMRI} data, particularly when the time courses of activation are not known in advance.},
	number = {3},
	journal = {Proceedings of the National Academy of Sciences of the United States of America},
	author = {{McKeown}, M J and Jung, T P and Makeig, S and Brown, G and Kindermann, S S and Lee, T W and Sejnowski, T J},
	month = feb,
	year = {1998},
	note = {{PMID:} 9448244},
	keywords = {algorithms, Brain, Brain Mapping, Color Perception Tests, Humans, Magnetic Resonance Imaging, Models, Neurological, Psychomotor Performance, Statistics as Topic},
	pages = {803--810}
},

@article{choi_blind_2000,
	title = {Blind Separation of Nonstationary Sources in Noisy Mixtures},
	url = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.34.4931},
	author = {Choi, Seungjin and Cichocki, Andrzej},
	year = {2000}
},

@article{horikis_fractal_2007,
	title = {Fractal self-transform functions},
	volume = {24},
	number = {1},
	journal = {{JOSA} A},
	author = {Horikis, {T.P.}},
	year = {2007},
	pages = {253--254}
},

@book{hyvarinen_independent_2001,
	title = {Independent component analysis},
	volume = {26},
	publisher = {Wiley-interscience},
	author = {Hyv\"arinen, A. and Karhunen, J. and Oja, E.},
	year = {2001}
},

@book{hyvarinen_independent_2001-1,
	title = {Independent component analysis},
	volume = {26},
	publisher = {Wiley-interscience},
	author = {Hyv\"arinen, A. and Karhunen, J. and Oja, E.},
	year = {2001}
},

@inproceedings{choi_natural_1999,
	title = {Natural gradient learning with a nonholonomic constraint for blind deconvolution of multiple channels},
	booktitle = {First International Workshop on Independent Component Analysis and Signal Separation},
	author = {Choi, S. and Amari, S. and Cichocki, A. and Liu, R.},
	year = {1999},
	pages = {371--376}
},

@article{piccard_couleurs_1918,
	title = {Couleurs de second ordre},
	volume = {1},
	doi = {10.1002/hlca.19180010112},
	number = {1},
	author = {Piccard, Jean},
	year = {1918},
	pages = {134--135}
},

@article{penny_ica:_2001,
	title = {{ICA:} model order selection and dynamic source models},
	shorttitle = {{ICA}},
	journal = {Independent component analysis: Principles and practice},
	author = {Penny, W. and Everson, R. and Roberts, S.},
	year = {2001},
	pages = {299--314}
},

@book{daubechies_ten_1992,
	title = {Ten lectures on wavelets},
	isbn = {9780898712742},
	abstract = {This monograph contains 10 lectures presented by Dr. Daubechies as the principal speaker at the 1990 {CBMS-NSF} Conference on Wavelets and Applications. Wavelets are a mathematical development that many experts think may revolutionize the world of information storage and retrieval. They are a fairly simple mathematical tool now being applied to the compression of data, such as fingerprints, weather satellite photographs, and medical x-rays - that were previously thought to be impossible to condense without losing crucial details. The opening chapter provides an overview of the main problems presented in the book. Following chapters discuss the theoretical and practical aspects of wavelet theory, including wavelet transforms, orthonormal bases of wavelets, and characterization of functional spaces by means of wavelets. The last chapter presents several topics under active research, as multidimensional wavelets, wavelet packet bases, and a construction of wavelets tailored to decompose functions defined in a finite interval.},
	publisher = {{SIAM}},
	author = {Daubechies, Ingrid},
	month = jun,
	year = {1992},
	keywords = {Congresses, Mathematics, Mathematics / Applied, Mathematics / Differential Equations, Mathematics / General, Mathematics / Mathematical Analysis, Mathematics / Probability \& Statistics / General, Ondelettes, Science / Waves \& Wave Mechanics, Technology \& Engineering / General, Wavelets {(Mathematics)}, Wavelets {(Mathematics)/} Congresses}
},

@article{jung_imaging_2001,
	title = {Imaging brain dynamics using independent component analysis},
	volume = {89},
	number = {7},
	journal = {Proceedings of the {IEEE}},
	author = {Jung, {T.P.} and Makeig, S. and {McKeown}, {M.J.} and Bell, {A.J.} and Lee, {T.W.} and Sejnowski, {T.J.}},
	year = {2001},
	pages = {1107--1122}
},

@article{tugnait_identification_1997,
	title = {Identification and deconvolution of multichannel linear {non-Gaussian} processes using higher order statistics and inverse filter criteria},
	volume = {45},
	number = {3},
	journal = {Signal Processing, {IEEE} Transactions on},
	author = {Tugnait, {J.K.}},
	year = {1997},
	pages = {658--672}
},

@inproceedings{joho_frequency_2003,
	title = {Frequency domain realization of a multichannel blind deconvolution algorithm based on the natural gradient},
	booktitle = {Proc. Int. Symp. {ICA}},
	author = {Joho, M. and Schniter, P. and others},
	year = {2003},
	pages = {543--548}
},

@article{amari_new_1996,
	title = {A new learning algorithm for blind signal separation},
	journal = {Advances in neural information processing systems},
	author = {Amari, S. and Cichocki, A. and Yang, {H.H.} and others},
	year = {1996},
	pages = {757--763}
},

@inproceedings{schobben_evaluation_1999,
	title = {Evaluation of blind signal separation methods},
	booktitle = {Proc. Int. Workshop on {ICA} and {BSS} {(ICA'99}},
	author = {Schobben, D. and Torkkola, K. and Smaragdis, P.},
	year = {1999},
	pages = {261--266}
},

@article{pajunen_maximum_1997,
	title = {A maximum likelihood approach to nonlinear blind source separation},
	journal = {Artificial Neural {Networks---ICANN'97}},
	author = {Pajunen, P. and Karhunen, J.},
	year = {1997},
	pages = {541--546}
},

@article{onton_imaging_2006,
	title = {Imaging human {EEG} dynamics using independent component analysis},
	volume = {30},
	number = {6},
	journal = {Neuroscience \& Biobehavioral Reviews},
	author = {Onton, J. and Westerfield, M. and Townsend, J. and Makeig, S.},
	year = {2006},
	pages = {808--822}
},

@article{bastiaans_sliding-window_1985,
	title = {On the sliding-window representation in digital signal processing},
	volume = {33},
	issn = {0096-3518},
	doi = {10.1109/TASSP.1985.1164653},
	abstract = {The short-time Fourier transform of a discrete-time signal, which is the Fourier transform of a "windowed" version of the signal, is interpreted as a sliding-window spectrum. This sliding-window spectrum is a function of two variables: a discrete time index, which represents the position of the window, and a continuous frequency variable. It is shown that the signal can be reconstructed from the sampled sliding-window spectrum, i.e., from the values at the points of a certain time-frequency lattice. This sampling lattice is rectangular, and the rectangular cells occupy an area of 2\emph{[U+03C0]} in the time-frequency domain. It is shown that an elegant way to represent the signal directly in terms of the sample values of the sliding-window spectrum, is in the form of Gabor's signal representation. Therefore, a reciprocal window is introduced, and it is shown how the window and the reciprocal window are related. Gabor's signal representation then expands the signal in terms of properly shifted and modulated versions of the reciprocal window, and the expansion coefficients are just the values of the sampled sliding-window spectrum.},
	number = {4},
	journal = {{IEEE} Transactions on Acoustics, Speech and Signal Processing},
	author = {Bastiaans, M.},
	month = aug,
	year = {1985},
	keywords = {Digital signal processing, Fourier transforms, Lattices, Sampling methods, Signal analysis, Signal processing, Signal representations, Speech, Time frequency analysis},
	pages = {868-- 873}
},

@article{cichocki_robust_1996,
	title = {Robust neural networks with on-line learning for blind identification and blind separation of sources},
	volume = {43},
	issn = {1057-7122},
	doi = {10.1109/81.542280},
	abstract = {Two unsupervised, self-normalizing, adaptive learning algorithms are developed for robust blind identification and/or blind separation of independent source signals from a linear mixture of them. One of these algorithms is developed for on-line learning of a single-layer feed-forward neural network model and a second one for a feedback (fully recurrent) neural network model. The proposed algorithms are robust, efficient, fast and suitable for real-time implementations. Moreover, they ensure the separation of extremely weak or badly scaled stationary signals, as well as a successful separation even if the mixture matrix is very ill-conditioned (near singular). The performance of the proposed algorithms is illustrated by computer simulation experiments},
	number = {11},
	journal = {{IEEE} Transactions on Circuits and Systems I: Fundamental Theory and Applications},
	author = {Cichocki, A. and Unbehauen, R.},
	month = nov,
	year = {1996},
	keywords = {Acoustic sensors, adaptive learning algorithms, adaptive signal processing, Biosensors, blind identification, blind separation, feedback neural network model, feedforward neural nets, Feedforward neural networks, fully recurrent neural network model, identification, independent source signals, Neural networks, Neurofeedback, online learning, real-time implementations, recurrent neural nets, Recurrent neural networks, robust neural networks, Robustness, Sensor arrays, Sensor phenomena and characterization, Signal processing, single-layer feedforward neural network model, Unsupervised learning, unsupervised self-normalizing learning algorithms},
	pages = {894--906}
},

@inproceedings{lacoume_sources_1988,
	title = {Sources indentification: a solution based on the cumulants},
	shorttitle = {Sources indentification},
	doi = {10.1109/SPECT.1988.206191},
	abstract = {It is noted that the problem of source separation has no solution without a priori information when only the spectral matrix is used. The authors have developed an algorithm using the fourth-order cumulants that solves the problem of the two-source separation without the necessity of a priori information. They show that, in the case of two sources and two sensors, the model is described by two parameters. They then present the identification of these two parameters with the cumulants up to order 4. The potentialities of this source-identification algorithm are illustrated using simulated data},
	booktitle = {, Fourth Annual {ASSP} Workshop on Spectrum Estimation and Modeling, 1988},
	publisher = {{IEEE}},
	author = {Lacoume, J. J and Ruiz, P.},
	month = aug,
	year = {1988},
	keywords = {Acoustic sensors, Array signal processing, cumulants, Filtering, identification, information theory, matrix algebra, parameter estimation, parameter identification, Sensor arrays, Signal processing, Source separation, sources identification, spectral matrix, Statistics, Stochastic processes, Underwater acoustics, Underwater vehicles},
	pages = {199--203}
},

@article{parra_convolutive_2000,
	title = {Convolutive blind separation of non-stationary sources},
	volume = {8},
	number = {3},
	journal = {Speech and Audio Processing, {IEEE} Transactions on},
	author = {Parra, L. and Spence, C.},
	year = {2000},
	pages = {320--327}
},

@article{douglas_convolutive_2003,
	title = {Convolutive blind separation of speech mixtures using the natural gradient},
	volume = {39},
	issn = {0167-6393},
	url = {http://dl.acm.org/citation.cfm?id=781587.781593},
	doi = {10.1016/S0167-6393(02)00059-6},
	number = {1-2},
	journal = {Speech Commun.},
	author = {Douglas, Scott C. and Sun, Xiaoan},
	month = jan,
	year = {2003},
	pages = {65--78}
},

@article{burel_blind_1992,
	title = {Blind separation of sources: A nonlinear neural algorithm},
	volume = {5},
	shorttitle = {Blind separation of sources},
	number = {6},
	journal = {Neural networks},
	author = {Burel, G.},
	year = {1992},
	pages = {937--947}
},

@article{cardoso_high-order_1999,
	title = {High-order contrasts for independent component analysis},
	volume = {11},
	number = {1},
	journal = {Neural computation},
	author = {Cardoso, {J.F.}},
	year = {1999},
	pages = {157--192}
},

@article{bastiaans_gabors_1980,
	title = {Gabor's expansion of a signal into Gaussian elementary signals},
	volume = {68},
	issn = {0018-9219},
	doi = {10.1109/PROC.1980.11686},
	abstract = {It is proved that Gabor's expansion of a signal into a discrete set of Gaussian elementary signals exists. An expansion into another discrete set of functions is defined, which functions are biorthonormal, to the Gaussian elementary signals. Hence, the expansion coefficients of the two expansions can be determined easily.},
	number = {4},
	journal = {Proceedings of the {IEEE}},
	author = {Bastiaans, M. J},
	month = apr,
	year = {1980},
	keywords = {Convolution, Delay lines, Electrons, Fourier transforms, Frequency, Integral equations, Mars, Phase modulation, Spread spectrum communication},
	pages = {538-- 539}
},

@inproceedings{cardoso_eigen-structure_1990,
	title = {Eigen-structure of the fourth-order cumulant tensor with application to the blind source separation problem},
	booktitle = {Acoustics, Speech, and Signal Processing, 1990. {ICASSP-90.}, 1990 International Conference on},
	author = {Cardoso, {J.F.}},
	year = {1990},
	pages = {2655--2658}
},

@article{belouchrani_robust_2000,
	title = {Robust whitening procedure in blind source separation context},
	volume = {36},
	number = {24},
	journal = {Electronics Letters},
	author = {Belouchrani, A. and Cichocki, A.},
	year = {2000},
	pages = {2050--2051}
},

@inproceedings{choi_blind_2000-1,
	title = {Blind separation of nonstationary and temporally correlated sources from noisy mixtures},
	volume = {1},
	booktitle = {Neural Networks for Signal Processing X, 2000. Proceedings of the 2000 {IEEE} Signal Processing Society Workshop},
	author = {Choi, S. and Cichocki, A.},
	year = {2000},
	pages = {405--414}
},

@article{wu_numerical_2001,
	title = {Numerical inversion of Laplace transform using Haar wavelet operational matrices},
	volume = {48},
	number = {1},
	journal = {Circuits and Systems I: Fundamental Theory and Applications, {IEEE} Transactions on},
	author = {Wu, {J.L.} and Chen, {C.H.} and Chen, {C.F.}},
	year = {2001},
	pages = {120--122}
},

@article{vigario_extraction_1997,
	title = {Extraction of ocular artefacts from {EEG} using independent component analysis},
	volume = {103},
	number = {3},
	journal = {Electroencephalography and clinical Neurophysiology},
	author = {Vig\'ario, {R.N.}},
	year = {1997},
	pages = {395--404}
},

@inproceedings{de_lathauwer_matrix_1998,
	title = {From matrix to tensor: Multilinear algebra and signal processing},
	volume = {67},
	shorttitle = {From matrix to tensor},
	booktitle = {{INSTITUTE} {OF} {MATHEMATICS} {AND} {ITS} {APPLICATIONS} {CONFERENCE} {SERIES}},
	author = {De Lathauwer, L. and De Moor, B.},
	year = {1998},
	pages = {1--16}
},

@book{haykin_kalman_2001,
	title = {Kalman filtering and neural networks},
	publisher = {Wiley Online Library},
	author = {Haykin, {S.S.} and others},
	year = {2001}
},

@article{dyrholm_model_2007,
	title = {Model selection for convolutive {ICA} with an application to spatiotemporal analysis of {EEG}},
	volume = {19},
	number = {4},
	journal = {Neural Computation},
	author = {Dyrholm, M. and Makeig, S. and Hansen, {L.K.}},
	year = {2007},
	pages = {934--955}
},

@article{karhunen_generalizations_1995,
	title = {Generalizations of principal component analysis, optimization problems, and neural networks},
	volume = {8},
	number = {4},
	journal = {Neural Networks},
	author = {Karhunen, J. and Joutsensalo, J.},
	year = {1995},
	pages = {549--562}
},

@article{millerioux_finite-time_2001,
	title = {Finite-time global chaos synchronization for piecewise linear maps},
	volume = {48},
	number = {1},
	journal = {Circuits and Systems I: Fundamental Theory and Applications, {IEEE} Transactions on},
	author = {Millerioux, G. and Mira, C.},
	year = {2001},
	pages = {111--116}
},

@article{marcaurelio_ranzato_efficient_2006,
	title = {Efficient learning of sparse representations with an energy-based model},
	volume = {19},
	journal = {Advances in neural information processing systems},
	author = {{Marc'Aurelio} Ranzato, {C.P.} and Chopra, S. and {LeCun}, Y.},
	year = {2006},
	pages = {1137--1144}
},

@article{shalvi_new_1990,
	title = {New criteria for blind deconvolution of nonminimum phase systems (channels)},
	volume = {36},
	number = {2},
	journal = {Information Theory, {IEEE} Transactions on},
	author = {Shalvi, O. and Weinstein, E.},
	year = {1990},
	pages = {312--321}
},

@inproceedings{waheed_state_2002,
	title = {State space blind source recovery for mixtures of multiple source distributions},
	volume = {1},
	booktitle = {Circuits and Systems, 2002 {IEEE} International Symposium on},
	author = {Waheed, K. and Salam, {F.M.}},
	year = {2002},
	pages = {I--197}
},

@article{barlow_ferrier_1981,
	title = {The Ferrier Lecture, 1980: Critical Limiting Factors in the Design of the Eye and Visual Cortex},
	volume = {212},
	issn = {0080-4649},
	shorttitle = {The Ferrier Lecture, 1980},
	url = {http://www.jstor.org/stable/35490},
	abstract = {The main factors limiting the performance of the peripheral parts of the visual system can be specified, and doing this clarifies the nature of the interpretive tasks that must be performed by the central parts of the system. It is argued that the critical factor that hinders development of better resolving power is the difficulty of confining light within the waveguide-like outer segment, and that for sensitivity this critical factor is the thermal decomposition of photosensitive pigments. Knowledge of these limits makes many surprising details of the eye intelligible. Understanding the difficulties posed by the narrow dynamic range of nerve fibres may give similar insight into the coding of the retinal image for transmission to the brain. Our level of understanding changes when we come to the visual cortex, for although we do not lack good anatomical and neurophysiological data, these do not make the principles of operation self-evident in the way that the structure of the eye immediately suggests that it is an image-forming device. The cortex converts the representation of the visual field that it receives into reliable knowledge of the world around us, and the trouble may be that we lack good models of how this can be done. A system that can respond to single quanta and resolve almost to the diffraction limit is unlikely to employ grossly inefficient methods for those higher functions upon which its whole utility depends, and so it is worth seeking out the limiting factors. The quality of human performance at certain higher perceptual tasks is high compared with the limit of reliable statistical inference; hence much of the sample of information available in a visual image must be effectively utilized. But there are strong limitations on the connectivity in the cortex, so that one is forced to consider how the relevant information can be collected together. Three stages of dealing with the visual image are proposed: the improvement of the cortical map in primary visual cortex by processes analogous to spatial and temporal interpolation; the detection of linking features in this map; and the concentration of this information by non-topographical mapping in adjacent visual areas.},
	number = {1186},
	journal = {Proceedings of the Royal Society of London. Series B, Biological Sciences},
	author = {Barlow, H. B.},
	month = may,
	year = {1981},
	note = {{ArticleType:} research-article / Full publication date: May 7, 1981 / Copyright \copyright{} 1981 The Royal Society},
	pages = {1--34}
},

@article{mansour_fourth-order_1995,
	title = {Fourth-order criteria for blind sources separation},
	volume = {43},
	number = {8},
	journal = {Signal Processing, {IEEE} Transactions on},
	author = {Mansour, A. and Jutten, C.},
	year = {1995},
	pages = {2022--2025}
},

@article{nandi_fourth-order_1996,
	title = {Fourth-order cumulant based blind source separation},
	volume = {3},
	number = {12},
	journal = {Signal Processing Letters, {IEEE}},
	author = {Nandi, {AK} and Zarzoso, V.},
	year = {1996},
	pages = {312--314}
},

@article{buchner_generalization_2005,
	title = {A generalization of blind source separation algorithms for convolutive mixtures based on second-order statistics},
	volume = {13},
	number = {1},
	journal = {Speech and Audio Processing, {IEEE} Transactions on},
	author = {Buchner, H. and Aichner, R. and Kellermann, W.},
	year = {2005},
	pages = {120--134}
},

@inproceedings{li_sparse_2003,
	title = {Sparse component analysis for blind source separation with less sensors than sources},
	booktitle = {{ICA2003}},
	author = {Li, Y. and Cichocki, A. and Amari, S.},
	year = {2003},
	pages = {89--94}
},

@article{riera_state-space_2004,
	title = {A state-space model of the hemodynamic approach: nonlinear filtering of {BOLD} signals},
	volume = {21},
	shorttitle = {A state-space model of the hemodynamic approach},
	number = {2},
	journal = {{NeuroImage}},
	author = {Riera, {J.J.} and Watanabe, J. and Kazuki, I. and Naoki, M. and Aubert, E. and Ozaki, T. and Kawashima, R.},
	year = {2004},
	pages = {547--567}
},

@article{amari_natural_1999,
	title = {Natural gradient learning for over-and under-complete bases in {ICA}},
	volume = {11},
	number = {8},
	journal = {Neural Computation},
	author = {Amari, {S.I.}},
	year = {1999},
	pages = {1875--1883}
},

@article{bastiaans_wigner_1979,
	title = {Wigner distribution function and its application to first-order optics},
	volume = {69},
	doi = {10.1364/JOSA.69.001710},
	number = {12},
	journal = {Journal of the Optical Society of America},
	author = {Bastiaans, M J},
	year = {1979},
	pages = {1710}
},

@article{harmeling_kernel-based_2003,
	title = {Kernel-based nonlinear blind source separation},
	volume = {15},
	number = {5},
	journal = {Neural Computation},
	author = {Harmeling, S. and Ziehe, A. and Kawanabe, M. and M\"uller, {K.R.}},
	year = {2003},
	pages = {1089--1124}
},

@article{gilmore_bakercampbellhausdorff_1974,
	title = {{Baker-Campbell-Hausdorff} formulas},
	volume = {15},
	issn = {00222488},
	url = {http://jmp.aip.org/resource/1/jmapaq/v15/i12/p2090_s1?isAuthorized=no},
	doi = {doi:10.1063/1.1666587},
	abstract = {{Baker-Campbell-Hausdorff} formulas can be constructed simply by matrix multiplication. Examples are given.},
	number = {12},
	journal = {Journal of Mathematical Physics},
	author = {Gilmore, R.},
	month = dec,
	year = {1974},
	pages = {2090--2092}
},

@article{zhang_semiparametric_2000,
	title = {Semiparametric Approach to Multichannel Blind Deconvolution of Non-minimum Phase Systems},
	volume = {12},
	journal = {Advances in Neural Information Processing Systems},
	author = {Zhang, {LQ} and Amari, S. and Cichocki, A.},
	year = {2000},
	pages = {363--369}
},

@article{calhoun_method_2001,
	title = {A method for making group inferences from functional {MRI} data using independent component analysis},
	volume = {14},
	number = {3},
	journal = {Human brain mapping},
	author = {Calhoun, {VD} and Adali, T. and Pearlson, {GD} and Pekar, {JJ}},
	year = {2001},
	pages = {140--151}
},

@article{smaragdis_blind_1998,
	title = {Blind separation of convolved mixtures in the frequency domain},
	volume = {22},
	number = {1},
	journal = {Neurocomputing},
	author = {Smaragdis, P. and others},
	year = {1998},
	pages = {21--34}
},

@article{thi_blind_1995,
	title = {Blind source separation for convolutive mixtures},
	volume = {45},
	number = {2},
	journal = {Signal processing},
	author = {Thi, {H.L.N.} and Jutten, C.},
	year = {1995},
	pages = {209--229}
},

@article{abed-meraim_blind_1997,
	title = {Blind system identification},
	volume = {85},
	number = {8},
	journal = {Proceedings of the {IEEE}},
	author = {{Abed-Meraim}, K. and Qiu, W. and Hua, Y.},
	year = {1997},
	pages = {1310--1322}
},

@article{engheta_fractional_1996,
	title = {On fractional calculus and fractional multipoles in electromagnetism},
	volume = {44},
	issn = {{0018-926X}},
	doi = {10.1109/8.489308},
	abstract = {Using the concept and tools of fractional calculus, we introduce a definition for ``fractional-order'' multipoles of electric-charge densities, and we show that as far as their scalar potential distributions are concerned, such fractional-order multipoles effectively behave as ``intermediate'' sources bridging the gap between the cases of integer-order point multipoles such as point monopoles, point dipoles, point quadrupoles, etc. This technique, which involves fractional differentiation or integration of the Dirac delta function, provides a tool for formulating an electric source distribution whose potential functions can be obtained by using fractional differentiation or integration of potentials of integer-order point-multipoles of lower or higher orders. As illustrative examples, the cases of three-dimensional (point source) and two-dimensional (line source) problems in electrostatics are treated in detail, and an extension to the time-harmonic case is also addressed. In the three-dimensional electrostatic example, we suggest an electric-charge distribution which can be regarded as an ``intermediate'' case between cases of the electric-point monopole (point charge) and the electric-point dipole (point dipole), and we present its electrostatic potential which behaves as {r-(1+\emph{[U+03B1]})P\emph{[U+03B1]}(-cos\emph{[U+03B8]})} where 0{\textbackslash}textless\emph{[U+03B1]}{\textbackslash}textless1 and P\emph{[U+03B1]}(\textperiodcentered{}) is the Legendre function of noninteger degree \emph{[U+03B1]}, thus denoting this charge distribution as a fractional 2\emph{[U+03B1]}-pole. At the two limiting cases of \emph{[U+03B1]}=0 and \emph{[U+03B1]}=1, this fractional 2\emph{[U+03B1]} -pole becomes the standard point monopole and point dipole, respectively. A corresponding intermediate fractional-order multipole is also given for the two-dimensional electrostatic case. Potential applications of this treatment to the image method in electrostatic problems are briefly mentioned. Physical insights and interpretation for such fractional-order 2\emph{[U+03B1]}-poles are also given},
	number = {4},
	journal = {{IEEE} Transactions on Antennas and Propagation},
	author = {Engheta, N.},
	month = apr,
	year = {1996},
	keywords = {2d problems, {3D} problems, Bibliographies, current density, Differential equations, differentiation, Dirac delta function, Educational institutions, electric charge densities, electric point dipoles, electric potential, electric source distribution, Electrodynamics, Electromagnetic propagation, electromagnetism, electrostatic potential, electrostatic problems, Electrostatics, Fractals, Fractional calculus, fractional differentiation, fractional integration, fractional multipoles, fractional order multipoles, History, image method, integer order point multipoles, integration, intermediate sources, Legendre function, Mathematics, point charge, point monopoles, point quadrupoles, potential functions, scalar potential distributions},
	pages = {554--566}
},

@inproceedings{yen_robust_1996,
	title = {Robust automatic speech recognition using a multi-channel signal separation front-end},
	volume = {3},
	booktitle = {Spoken Language, 1996. {ICSLP} 96. Proceedings., Fourth International Conference on},
	author = {Yen, {K.C.} and Zhao, Y.},
	year = {1996},
	pages = {1337--1340}
},

@book{brandstein_microphone_2001,
	title = {Microphone arrays: signal processing techniques and applications},
	shorttitle = {Microphone arrays},
	publisher = {Springer Verlag},
	author = {Brandstein, M. and Ward, D.},
	year = {2001}
},

@article{zhang_extended_2005,
	title = {Extended gaussianization method for blind separation of post-nonlinear mixtures},
	volume = {17},
	number = {2},
	journal = {Neural computation},
	author = {Zhang, K. and Chan, {L.W.}},
	year = {2005},
	pages = {425--452}
},

@inproceedings{inouye_-line_1997,
	address = {Washington, {DC}, {USA}},
	series = {{SPWHOS} '97},
	title = {On-line Algorithms for Blind Deconvolution of Multichannel Linear {Time-Invariant} Systems},
	isbn = {0-8186-8005-9},
	url = {http://dl.acm.org/citation.cfm?id=882492.884341},
	booktitle = {Proceedings of the 1997 {IEEE} Signal Processing Workshop on {Higher-Order} Statistics {(SPW-HOS} '97)},
	publisher = {{IEEE} Computer Society},
	author = {Inouye, Yujiro and Sato, Takehito},
	year = {1997},
	pages = {204--}
},

@inproceedings{zhang_geometrical_1999,
	title = {Geometrical structures of {FIR} manifold and their application to multichannel blind deconvolution},
	booktitle = {Neural Networks for Signal Processing {IX}, 1999. Proceedings of the 1999 {IEEE} Signal Processing Society Workshop},
	author = {Zhang, {L.Q.} and Cichocki, A. and Amari, S.},
	year = {1999},
	pages = {303--312}
},

@article{abe_almost-fourier_1995,
	title = {{Almost-Fourier} and {almost-Fresnel} transformations},
	volume = {113},
	issn = {0030-4018},
	url = {http://www.sciencedirect.com/science/article/pii/003040189400521U},
	doi = {10.1016/0030-4018(94)00521-U},
	abstract = {Based on the special affine Fourier transformation {(SAFT)}, which is an extension of the fractional Fourier transformation, the small deviations from the perfect optical operations on wave functions are treated in a unified way. As simple and instructive examples, the {almost-Fourier} and {almost-Fresnel} transformations are constructed explicitly. The present investigation shows transformations which include the small imperfections of optical instruments can be developed within the framework of linear theory.},
	number = {4--6},
	journal = {Optics Communications},
	author = {Abe, Sumiyoshi and Sheridan, John T.},
	month = jan,
	year = {1995},
	pages = {385--388}
},

@article{jutten_advances_2004,
	title = {Advances in blind source separation {(BSS)} and independent component analysis {(ICA)} for nonlinear mixtures},
	volume = {14},
	number = {5},
	journal = {International Journal of Neural Systems},
	author = {Jutten, C. and Karhunen, J.},
	year = {2004},
	pages = {267--292}
},

@inproceedings{farah_satellite_2002,
	title = {Satellite image analysis based on the method of blind separation of sources for the extraction of information},
	volume = {2},
	booktitle = {Geoscience and Remote Sensing Symposium, 2002. {IGARSS'02.} 2002 {IEEE} International},
	author = {Farah, {IR} and Ahmed, {MB}},
	year = {2002},
	pages = {919--921}
},

@article{de_lathauwer_multilinear_2000,
	title = {A multilinear singular value decomposition},
	volume = {21},
	number = {4},
	journal = {{SIAM} Journal on Matrix Analysis and Applications},
	author = {De Lathauwer, L. and De Moor, B. and Vandewalle, J.},
	year = {2000},
	pages = {1253--1278}
},

@inproceedings{reyes-gomez_multi-channel_2003,
	title = {Multi-channel source separation by beamforming trained with factorial {HMMs}},
	booktitle = {Applications of Signal Processing to Audio and Acoustics, 2003 {IEEE} Workshop on.},
	author = {{Reyes-Gomez}, {M.J.} and Bhiksha, R. and Ellis, {D.P.W.}},
	year = {2003},
	pages = {13--16}
},

@article{hyvarinen_gaussian_1999,
	title = {Gaussian moments for noisy independent component analysis},
	volume = {6},
	number = {6},
	journal = {Signal Processing Letters, {IEEE}},
	author = {Hyvarinen, A.},
	year = {1999},
	pages = {145--147}
},

@inproceedings{lee_blind_1997-1,
	title = {Blind source separation of nonlinear mixing models},
	booktitle = {Neural Networks for Signal Processing [1997] {VII.} Proceedings of the 1997 {IEEE} Workshop},
	author = {Lee, {T.W.} and Koehler, {B.U.} and Orglmeister, R.},
	year = {1997},
	pages = {406--415}
},

@article{dobrea_application_????,
	title = {An Application of a Neuronal Method Used to Remove Artefacts},
	author = {Dobrea, {D.M.} and Dobrea, {M.C.}}
},

@article{lewicki_learning_2000,
	title = {Learning overcomplete representations},
	volume = {12},
	number = {2},
	journal = {Neural computation},
	author = {Lewicki, {M.S.} and Sejnowski, {T.J.}},
	year = {2000},
	pages = {337--365}
},

@article{mohamed_contribution_2007,
	title = {Contribution \`a la s\'eparation aveugle de sources par utilisation des divergences entre densit\'es de probabilit\'e : application \`a l'analyse vibratoire},
	shorttitle = {Contribution \`a la s\'eparation aveugle de sources par utilisation des divergences entre densit\'es de probabilit\'e},
	author = {Mohamed, Ould},
	year = {2007}
},

@inproceedings{karhunen_applications_1997,
	title = {Applications of neural blind separation to signal and image processing},
	volume = {1},
	booktitle = {Acoustics, Speech, and Signal Processing, 1997. {ICASSP-97.}, 1997 {IEEE} International Conference on},
	author = {Karhunen, J. and Hyvarinen, A. and Vig\'ario, R. and Hurri, J. and Oja, E.},
	year = {1997},
	pages = {131--134}
},

@article{kuruoglu_bayesian_2010,
	title = {Bayesian source separation for cosmology},
	volume = {27},
	number = {1},
	journal = {Signal Processing Magazine, {IEEE}},
	author = {Kuruoglu, E.},
	year = {2010},
	pages = {43--54}
},

@article{bell_information-maximization_1995,
	title = {An information-maximization approach to blind separation and blind deconvolution},
	volume = {7},
	number = {6},
	journal = {Neural computation},
	author = {Bell, {A.J.} and Sejnowski, {T.J.}},
	year = {1995},
	pages = {1129--1159}
},

@article{matsuoka_neural_1995,
	title = {A neural net for blind separation of nonstationary signals},
	volume = {8},
	issn = {0893-6080},
	url = {http://www.sciencedirect.com/science/article/pii/089360809400083X},
	doi = {10.1016/0893-6080(94)00083-X},
	abstract = {This paper proposes a neural network that recovers some original random signals from their linear mixtures observed by the same number of sensors. The network acquires the function with a learning process without using any particular information about the statistical properties of the sources and the coefficients of the linear transformation, except the fact that the source signals are statistically independent and nonstationary. The learning rule for the network's parameters is derived from the steepest descent minimization of a time-dependent cost function that takes the minimum only when the network outputs are uncorrelated with each other.},
	number = {3},
	journal = {Neural Networks},
	author = {Matsuoka, Kiyotoshi and Ohoya, Masahiro and Kawamoto, Mitsuru},
	year = {1995},
	keywords = {{Anti-Hebbian} learning, blind separation, Nonstationary signals, Self-organization},
	pages = {411--419}
},

@article{van_gerven_signal_1995,
	title = {Signal separation by symmetric adaptive decorrelation: stability, convergence, and uniqueness},
	volume = {43},
	shorttitle = {Signal separation by symmetric adaptive decorrelation},
	number = {7},
	journal = {Signal Processing, {IEEE} Transactions on},
	author = {Van Gerven, S. and Van Compernolle, D.},
	year = {1995},
	pages = {1602--1612}
},

@article{ehlers_blind_1997,
	title = {Blind separation of convolutive mixtures and an application in automatic speech recognition in a noisy environment},
	volume = {45},
	number = {10},
	journal = {Signal Processing, {IEEE} Transactions on},
	author = {Ehlers, F. and Schuster, {HG}},
	year = {1997},
	pages = {2608--2612}
},

@inproceedings{vigario_independent_1998,
	address = {Cambridge, {MA}, {USA}},
	series = {{NIPS} '97},
	title = {Independent component analysis for identification of artifacts in magnetoencephalographic recordings},
	isbn = {0-262-10076-2},
	url = {http://dl.acm.org/citation.cfm?id=302528.302596},
	booktitle = {Proceedings of the 1997 conference on Advances in neural information processing systems 10},
	publisher = {{MIT} Press},
	author = {Vig\'ario, Ricardo and Jousm\'aki, Veikko and H\"am\"al\"ainen, Matti and Hari, Riitta and Oja, Erkki},
	year = {1998},
	pages = {229--235}
},

@inproceedings{cichocki_-line_1997,
	title = {On-line adaptive algorithms in non-stationary environments using a modified conjugate gradient approach},
	booktitle = {Neural Networks for Signal Processing [1997] {VII.} Proceedings of the 1997 {IEEE} Workshop},
	author = {Cichocki, A. and Orsier, B. and Back, A. and Amari, {S.I.}},
	year = {1997},
	pages = {316--325}
},

@article{cichocki_-line_2000,
	title = {On-line algorithm for blind signal extraction of arbitrarily distributed, but temporally correlated sources using second order statistics},
	volume = {12},
	number = {1},
	journal = {Neural Processing Letters},
	author = {Cichocki, A. and Thawonmas, R.},
	year = {2000},
	pages = {91--98}
},

@article{calhoun_independent_2004,
	title = {Independent component analysis applied to {fMRI} data: a generative model for validating results},
	volume = {37},
	shorttitle = {Independent component analysis applied to {fMRI} data},
	number = {2},
	journal = {The Journal of {VLSI} Signal Processing},
	author = {Calhoun, V. and Pearlson, G. and Adali, T.},
	year = {2004},
	pages = {281--291}
},

@book{de_lathauwer_signal_1997,
	title = {Signal processing based on multilinear algebra},
	publisher = {Katholieke Universiteit Leuven},
	author = {De Lathauwer, L.},
	year = {1997}
},

@inproceedings{ziehe_separation_2001,
	title = {Separation of post-nonlinear mixtures using {ACE} and temporal decorrelation},
	booktitle = {Proc. Int. Workshop on Independent Component Analysis and Blind Signal Separation {(ICA2001)}},
	author = {Ziehe, A. and Kawanabe, M. and Harmeling, S. and M\"uller, {K.R.}},
	year = {2001},
	pages = {433--438}
},

@article{yellin_criteria_1994,
	title = {Criteria for multichannel signal separation},
	volume = {42},
	number = {8},
	journal = {Signal Processing, {IEEE} Transactions on},
	author = {Yellin, D. and Weinstein, E.},
	year = {1994},
	pages = {2158--2168}
},


@book{hyvarinen_independent_2001-2,
	title = {Independent component analysis},
	volume = {26},
	publisher = {Wiley-interscience},
	author = {Hyv\"arinen, A. and Karhunen, J. and Oja, E.},
	year = {2001}
},

@inproceedings{chan_multi-channel_1996,
	title = {Multi-channel signal separation},
	volume = {2},
	booktitle = {Acoustics, Speech, and Signal Processing, 1996. {ICASSP-96.} Conference Proceedings., 1996 {IEEE} International Conference on},
	author = {Chan, {D.C.B.} and Rayner, {P.J.W.} and Godsill, {S.J.}},
	year = {1996},
	pages = {649--652}
},

@book{weinstein_multi-channel_1996,
	title = {Multi-channel signal separation using cross-polyspectra},
	publisher = {Google Patents},
	author = {Weinstein, E. and Yellin, D.},
	month = jul,
	year = {1996},
	note = {{US} Patent 5,539,832}
},

@article{ozaktas_fractional_1993,
	title = {Fractional Fourier transforms and their optical implementation. {II}},
	volume = {10},
	number = {12},
	journal = {{JOSA} A},
	author = {Ozaktas, {H.M.} and Mendlovic, D.},
	year = {1993},
	pages = {2522--2531}
},

@article{amari_stability_1997,
	title = {Stability analysis of learning algorithms for blind source separation},
	volume = {10},
	number = {8},
	journal = {Neural Networks},
	author = {Amari, {S.I.} and Chen, {T.P.} and Cichocki, A.},
	year = {1997},
	pages = {1345--1351}
},

@article{zhang_blind_1999,
	title = {Blind Separation of Filtered Sources Using {State-Space} Approach},
	volume = {11},
	url = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.55.5178},
	journal = {{ADVANCES} {IN} {NEURAL} {INFORMATION} {PROCESSING} {SYSTEMS}},
	author = {Zhang, Liqing and Cichocki, Andrzej},
	year = {1999},
	pages = {648---654}
},

@misc{_source_????,
	title = {Source separation - Wikipedia, the free encyclopedia},
	url = {http://en.wikipedia.org/wiki/Source_separation},
	howpublished = {{http://en.wikipedia.org/wiki/Source\_separation}}
},

@inproceedings{shynk_blind_1990,
	title = {Blind adaptive filtering in the frequency domain},
	doi = {10.1109/ISCAS.1990.112008},
	abstract = {Efficient block implementations which are based on frequency-domain techniques of the constant modulus and the P-vector least-mean-square blind algorithms are described. These realizations have much less computational complexity than nonblock time-domain methods, and they can have improved convergence properties. Although several implementations are possible, including filter-bank and multirate structures, only linear convolution methods are used. A frequency-domain realization of a T/2-spaced constant modulus algorithm equalizer is examined, and computer simulations are presented},
	booktitle = {, {IEEE} International Symposium on Circuits and Systems, 1990},
	publisher = {{IEEE}},
	author = {Shynk, J. J and Chan, C. K and Petraglia, M. R},
	month = may,
	year = {1990},
	keywords = {Adaptive algorithm, Adaptive filters, block implementations, computational complexity, Computer errors, constant modulus, convergence, convergence properties, Convolution, Estimation error, filtering and prediction theory, frequency domain, Frequency domain analysis, frequency-domain analysis, frequency-domain realization, Information processing, Least squares approximation, linear convolution methods, P-vector least-mean-square blind algorithms, Signal processing},
	pages = {275--278 vol.1}
},

@article{comon_independent_1994,
	title = {Independent component analysis, a new concept?},
	volume = {36},
	number = {3},
	journal = {Signal processing},
	author = {Comon, P.},
	year = {1994},
	pages = {287--314}
},

@book{giannakis_signal_2000,
	edition = {1st},
	title = {Signal Processing Advances in Wireless and Mobile Communications, Volume 1: Trends in Channel Estimation and Equalization},
	isbn = {0130271624},
	shorttitle = {Signal Processing Advances in Wireless and Mobile Communications, Volume 1},
	publisher = {Prentice Hall {PTR}},
	author = {Giannakis, Georgios B. and Hua, Yingbo and Stoica, Petre and Tong, Lang},
	month = oct,
	year = {2000}
},

@article{farina_blind_2004,
	title = {Blind separation of linear instantaneous mixtures of nonstationary surface myoelectric signals},
	volume = {51},
	number = {9},
	journal = {Biomedical Engineering, {IEEE} Transactions on},
	author = {Farina, D. and F\'evotte, C. and Doncarli, C. and Merletti, R.},
	year = {2004},
	pages = {1555--1567}
},

@inproceedings{cardoso_blind_1993,
	title = {Blind beamforming for {non-Gaussian} signals},
	volume = {140},
	booktitle = {Radar and Signal Processing, {IEE} Proceedings F},
	author = {Cardoso, {J.F.} and Souloumiac, A.},
	year = {1993},
	pages = {362--370}
},

@article{beckmann_investigations_2005,
	title = {Investigations into resting-state connectivity using independent component analysis},
	volume = {360},
	number = {1457},
	journal = {Philosophical Transactions of the Royal Society B: Biological Sciences},
	author = {Beckmann, {C.F.} and {DeLuca}, M. and Devlin, {J.T.} and Smith, {S.M.}},
	year = {2005},
	pages = {1001--1013}
},

@article{choi_equivariant_2002,
	title = {Equivariant nonstationary source separation},
	volume = {15},
	number = {1},
	journal = {Neural Networks},
	author = {Choi, S. and Cichocki, A. and Amari, S.},
	year = {2002},
	pages = {121--130}
},

@techreport{porrill_undercomplete_1998,
	title = {Undercomplete independent component analysis for signal separation and dimension reduction},
	institution = {Citeseer},
	author = {Porrill, J. and Stone, {J.V.}},
	year = {1998}
},

@article{bijaoui_blind_2001,
	title = {Blind source separation of multispectral astronomical images},
	journal = {Mining the Sky},
	author = {Bijaoui, A. and Nuzillard, D.},
	year = {2001},
	pages = {571--581}
},

@article{hyvarinen_independent_2009,
	title = {Independent component analysis},
	journal = {Natural Image Statistics},
	author = {Hyv\"arinen, A. and Hurri, J. and Hoyer, {P.O.}},
	year = {2009},
	pages = {151--175}
},

@article{vinjamuri_extraction_2009,
	title = {Extraction of sources of tremor in hand movements of patients with movement disorders},
	volume = {13},
	number = {1},
	journal = {Information Technology in Biomedicine, {IEEE} Transactions on},
	author = {Vinjamuri, R. and Crammond, {D.J.} and Kondziolka, D. and Lee, {H.N.} and Mao, {Z.H.}},
	year = {2009},
	pages = {49--56}
},

@article{mckeown_independent_2003,
	title = {Independent component analysis of functional {MRI:} what is signal and what is noise?},
	volume = {13},
	shorttitle = {Independent component analysis of functional {MRI}},
	number = {5},
	journal = {Current Opinion in Neurobiology},
	author = {{McKeown}, {M.J.} and Hansen, {L.K.} and Sejnowsk, {T.J.}},
	year = {2003},
	pages = {620--629}
},

@article{raiko_state_2006,
	title = {State inference in variational Bayesian nonlinear state-space models},
	journal = {Independent Component Analysis and Blind Signal Separation},
	author = {Raiko, T. and Tornio, M. and Honkela, A. and Karhunen, J.},
	year = {2006},
	pages = {222--229}
},

@inproceedings{robila_fast_2002,
	title = {A fast source separation algorithm for hyperspectral image processing},
	volume = {6},
	booktitle = {Geoscience and Remote Sensing Symposium, 2002. {IGARSS'02.} 2002 {IEEE} International},
	author = {Robila, {S.A.} and Varshney, {P.K.}},
	year = {2002},
	pages = {3516--3518}
},

@article{rojas_blind_2004,
	title = {Blind source separation in post-nonlinear mixtures using competitive learning, simulated annealing, and a genetic algorithm},
	volume = {34},
	number = {4},
	journal = {Systems, Man, and Cybernetics, Part C: Applications and Reviews, {IEEE} Transactions on},
	author = {Rojas, F. and Puntonet, {C.G.} and Rodr\'{\i}guez-\'Alvarez, M. and Rojas, I. and {Mart{\textbackslash}textbackslash'in-Clemente}, R.},
	year = {2004},
	pages = {407--416}
},

@misc{center_for_history_and_new_media_zotero_????,
	title = {Zotero Quick Start Guide},
	url = {http://zotero.org/support/quick_start_guide},
	author = {{{Center} for History and New Media}},
	howpublished = {http://zotero.org/support/quick\_start\_guide},
	annote = {Welcome to Zotero!

View the Quick Start Guide to learn how to begin collecting, managing, citing, and sharing your research sources.

Thanks for installing Zotero.}
},

@article{sahin_optical_1998,
	title = {Optical implementations of two-dimensional fractional Fourier transforms and linear canonical transforms with arbitrary parameters},
	volume = {37},
	number = {11},
	journal = {Applied optics},
	author = {Sahin, A. and Ozaktas, {H.M.} and Mendlovic, D.},
	year = {1998},
	pages = {2130--2141}
},

@inproceedings{cardoso_source_1989,
	title = {Source separation using higher order moments},
	booktitle = {Acoustics, Speech, and Signal Processing, 1989. {ICASSP-89.}, 1989 International Conference on},
	author = {Cardoso, {J.F.}},
	year = {1989},
	pages = {2109--2112}
},

@inproceedings{cichocki_blind_1999,
	title = {Blind separation and filtering using state space models},
	volume = {5},
	booktitle = {Circuits and Systems, 1999. {ISCAS'99.} Proceedings of the 1999 {IEEE} International Symposium on},
	author = {Cichocki, A. and Zhang, L. and Rutkowski, T.},
	year = {1999},
	keywords = {adaptive signal processing, Biomedical signal processing, biomedical signals, Blind equalizers, blind filtering, blind separation, Brain modeling, Deconvolution, {ECG}, {EEG}, Electroencephalography, {EMG}, {EOG}, Filtering, filtering theory, Geophysical signal processing, Independent component analysis, medical signal processing, {MEG}, multichannel blind deconvolution, mutual independence, nonlinear mixture hyper radial basis function, output signals, signal detection, Signal processing, source localization, speech enhancement, speech processing, state space models, State-space methods, supervised-unsupervised learning rules, unsupervised adaptive learning algorithms, wireless communications},
	pages = {78--81}
},

@article{lin_adaptive_2006,
	title = {Adaptive {EEG-based} alertness estimation system by using {ICA-based} fuzzy neural networks},
	volume = {53},
	number = {11},
	journal = {Circuits and Systems I: Regular Papers, {IEEE} Transactions on},
	author = {Lin, {C.T.} and Ko, {L.W.} and Chung, {I.F.} and Huang, {T.Y.} and Chen, {Y.C.} and Jung, {T.P.} and Liang, {S.F.}},
	year = {2006},
	pages = {2469--2476}
},

@article{everson_independent_1999,
	title = {Independent component analysis: A flexible nonlinearity and decorrelating manifold approach},
	volume = {11},
	shorttitle = {Independent component analysis},
	number = {8},
	journal = {Neural computation},
	author = {Everson, R. and Roberts, S.},
	year = {1999},
	pages = {1957--1983}
},

@article{plumbley_algorithms_2003,
	title = {Algorithms for nonnegative independent component analysis},
	volume = {14},
	issn = {1045-9227},
	doi = {10.1109/TNN.2003.810616},
	abstract = {We consider the task of solving the independent component analysis {(ICA)} problem {x=As} given observations x, with a constraint of nonnegativity of the source random vector s. We refer to this as nonnegative independent component analysis and we consider methods for solving this task. For independent sources with nonzero probability density function (pdf) p(s) down to s=0 it is sufficient to find the orthonormal rotation {y=Wz} of prewhitened sources {z=Vx}, which minimizes the mean squared error of the reconstruction of z from the rectified version y+ of y. We suggest some algorithms which perform this, both based on a nonlinear principal component analysis {(PCA)} approach and on a geodesic search method driven by differential geometry considerations. We demonstrate the operation of these algorithms on an image separation problem, which shows in particular the fast convergence of the rotation and geodesic methods and apply the approach to a musical audio analysis task.},
	number = {3},
	journal = {{IEEE} Transactions on Neural Networks},
	author = {Plumbley, M. D},
	month = may,
	year = {2003},
	keywords = {Algorithm design and analysis, differential geometry, geodesic search, Image analysis, Image reconstruction, image separation problem, Independent component analysis, mean squared error, musical audio analysis task, neural nets, Neural networks, nonnegative independent component analysis, nonzero probability density function, Principal component analysis, Probability density function, Search methods, Signal processing, Signal processing algorithms, source random vector, Stiefel manifold, Vectors},
	pages = {534-- 543}
},

@inproceedings{principe_temporal_1996,
	title = {Temporal decorrelation using teacher forcing {anti-Hebbian} learning and its application in adaptive blind source separation},
	booktitle = {Neural Networks for Signal Processing [1996] {VI.} Proceedings of the 1996 {IEEE} Signal Processing Society Workshop},
	author = {Principe, {J.C.} and Wang, C. and Wu, {H.C.}},
	year = {1996},
	pages = {413--422}
},

@inproceedings{de_lathauwer_independent_1996,
	title = {Independent component analysis based on higher-order statistics only},
	booktitle = {Statistical Signal and Array Processing, 1996. Proceedings., 8th {IEEE} Signal Processing Workshop on {(Cat.} No. {96TB10004}},
	author = {De Lathauwer, L. and De Moor, B. and Vandewalle, J.},
	year = {1996},
	keywords = {additive Gaussian noise, Additive noise, algorithms, Blind source separation, canonical decomposition, congruence transformation, correlation methods, data decorrelation, decorrelation, Gaussian noise, Givens type iteration, higher order only technique, Higher order statistics, higher-order cumulant tensor, identification, identification problem, Independent component analysis, Information processing, iterative methods, Jacobian matrices, linear algebra, matrix algebra, Matrix decomposition, matrix diagonalization, multilinear algebra, prewhitening, Signal processing, simultaneous Schur decomposition, statistical analysis, Tensile stress, white noise},
	pages = {356--359}
},

@article{cichocki_robust_1998,
	title = {Robust techniques for independent component analysis {(ICA)} with noisy data},
	volume = {22},
	number = {1},
	journal = {Neurocomputing},
	author = {Cichocki, A. and Douglas, {SC} and Amari, S.},
	year = {1998},
	pages = {113--130}
},

@article{giannakopoulos_comparison_1998,
	title = {Comparison of adaptive independent component analysis algorithms},
	author = {Giannakopoulos, X.},
	year = {1998}
},

@article{hyvarinen_independent_1998,
	title = {Independent component analysis by general nonlinear Hebbian-like learning rules},
	volume = {64},
	number = {3},
	journal = {signal processing},
	author = {Hyv\"arinen, A. and Oja, E.},
	year = {1998},
	pages = {301--313}
},

@book{groot_probability_1975,
	title = {Probability and Statistics},
	isbn = {{020101503X}},
	publisher = {{Addison-Wesley} Educational Publishers Inc},
	author = {Groot, Morris De},
	month = dec,
	year = {1975}
},

@article{ozaktas_convolution_1994,
	title = {Convolution, filtering, and multiplexing in fractional Fourier domains and their relation to chirp and wavelet transforms},
	volume = {11},
	number = {2},
	journal = {{JOSA} A},
	author = {Ozaktas, {H.M.} and Barshan, B. and Mendlovic, D. and Onural, L.},
	year = {1994},
	pages = {547--559}
},

@article{gribonval_survey_2006,
	title = {A survey of sparse component analysis for blind source separation: principles, perspectives, and new challenges},
	shorttitle = {A survey of sparse component analysis for blind source separation},
	author = {Gribonval, R. and Lesage, S. and others},
	year = {2006}
},

@article{moudden_iterative_2008,
	title = {An iterative thresholding algorithm for joint deconvolution and separation of multichannel data},
	journal = {{ADA} V, Heraklion},
	author = {Moudden, Y. and Bobin, J. and Starck, {JL}},
	year = {2008}
},

@article{amari_adaptive_1998,
	title = {Adaptive blind signal processing-neural network approaches},
	volume = {86},
	number = {10},
	journal = {Proceedings of the {IEEE}},
	author = {Amari, {S.I.} and Cichocki, A.},
	year = {1998},
	pages = {2026--2048}
},

@article{pham_blind_2001,
	title = {Blind separation of instantaneous mixture of sources via the Gaussian mutual information criterion},
	volume = {81},
	number = {4},
	journal = {Signal Processing},
	author = {Pham, {D.T.}},
	year = {2001},
	pages = {855--870}
},

@inproceedings{cichocki_new_1994,
	title = {A new on-line adaptive learning algorithm for blind separation of source signals},
	booktitle = {Int. Symp. on Artificial Neural Networks},
	author = {Cichocki, A. and Unbehauen, R. and Moszczynski, L. and Rummert, E.},
	year = {1994},
	pages = {406--411}
},

@article{miwakeichi_decomposing_2004,
	title = {Decomposing {EEG} data into space-time-frequency components using parallel factor analysis},
	volume = {22},
	number = {3},
	journal = {{NeuroImage}},
	author = {Miwakeichi, F. and Mart {nez-Montes}, E. and Vald {s-Sosa}, {P.A.} and Nishiyama, N. and Mizuhara, H. and Yamaguchi, Y.},
	year = {2004},
	pages = {1035--1045}
},

@inproceedings{jung_independent_2000,
	title = {Independent component analysis of biomedical signals},
	booktitle = {Proc. Int. Workshop on Independent Component Analysis and Signal Separation},
	author = {Jung, {T.P.} and Makeig, S. and Lee, {T.W.} and {McKeown}, {M.J.} and Brown, G. and Bell, {A.J.} and Sejnowski, {T.J.}},
	year = {2000},
	pages = {633--644}
},

@article{sejnowski_information-maximization_1995,
	title = {An information-maximization approach to blind separation and blind deconvolution},
	volume = {7},
	journal = {Neural Comput},
	author = {Sejnowski, {A.J.B.T.J.}},
	year = {1995},
	pages = {1129--1159}
},

@article{santoso_power_1996,
	title = {Power quality assessment via wavelet transform analysis},
	volume = {11},
	number = {2},
	journal = {Power Delivery, {IEEE} Transactions on},
	author = {Santoso, S. and Powers, {E.J.} and Grady, {W.M.} and Hofmann, P.},
	year = {1996},
	pages = {924--930}
},

@inproceedings{sun_natural_2001,
	title = {A natural gradient convolutive blind source separation algorithm for speech mixtures},
	volume = {1},
	booktitle = {Proc. {ICA}},
	author = {Sun, X. and Douglas, S.},
	year = {2001},
	pages = {59--64}
},

@article{georgiev_sparse_2005,
	title = {Sparse component analysis and blind source separation of underdetermined mixtures},
	volume = {16},
	number = {4},
	journal = {Neural Networks, {IEEE} Transactions on},
	author = {Georgiev, P. and Theis, F. and Cichocki, A.},
	year = {2005},
	pages = {992--996}
},

@article{ricardo_nuno_extraction_1997,
	title = {Extraction of ocular artefacts from {EEG} using independent component analysis},
	volume = {103},
	issn = {0013-4694},
	url = {http://www.sciencedirect.com/science/article/pii/S0013469497000428},
	doi = {10.1016/S0013-4694(97)00042-8},
	abstract = {Eye activity is one of the main sources of artefacts in {EEG} and {MEG} recordings. A new approach to the correction of these disturbances is presented using the statistical technique of independent component analysis. This technique separates components by the kurtosis of their amplitude distribution over time, thereby distinguishing between strictly periodical signals, regularly occurring signals and irregularly occurring signals. The latter category is usually formed by artefacts. Through this approach, it is possible to isolate pure eye activity in the {EEG} recordings (including {EOG} channels), and so reduce the amount of brain activity that is subtracted from the measurements, when extracting portions of the {EOG} signals.},
	number = {3},
	journal = {Electroencephalography and Clinical Neurophysiology},
	author = {Ricardo Nuno, Vig\'ario},
	month = sep,
	year = {1997},
	keywords = {Blind source separation {(BSS)}, {EEG}, {EOG}, Independent component analysis {(ICA)}, Ocular artefact correction},
	pages = {395--404}
},

@article{daubechies_wavelet_1990,
	title = {The wavelet transform, time-frequency localization and signal analysis},
	volume = {36},
	number = {5},
	journal = {Information Theory, {IEEE} Transactions on},
	author = {Daubechies, I.},
	year = {1990},
	pages = {961--1005}
},

@article{boutayeb_generalized_2002,
	title = {Generalized state-space observers for chaotic synchronization and secure communication},
	volume = {49},
	number = {3},
	journal = {Circuits and Systems I: Fundamental Theory and Applications, {IEEE} Transactions on},
	author = {Boutayeb, M. and Darouach, M. and Rafaralahy, H.},
	year = {2002},
	pages = {345--349}
},

@article{plett_adaptive_2003,
	title = {Adaptive inverse control of linear and nonlinear systems using dynamic neural networks},
	volume = {14},
	number = {2},
	journal = {Neural Networks, {IEEE} Transactions on},
	author = {Plett, {G.L.}},
	year = {2003},
	pages = {360--376}
},

@inproceedings{zhang_natural_2000,
	title = {Natural gradient approach to blind deconvolution of dynamical systems},
	booktitle = {Proceeding of {ICA'2000}},
	author = {Zhang, L. and Cichocki, A.},
	year = {2000},
	pages = {27--32}
},

@article{karhunen_representation_1994,
	title = {Representation and separation of signals using nonlinear {PCA} type learning},
	volume = {7},
	number = {1},
	journal = {Neural networks},
	author = {Karhunen, J. and Joutsensalo, J.},
	year = {1994},
	pages = {113--127}
},

@inproceedings{sidiropoulos_deterministic_1998,
	title = {Deterministic waveform-preserving blind separation of {DS-CDMA} signals using an antenna array},
	booktitle = {Statistical Signal and Array Processing, 1998. Proceedings., Ninth {IEEE} {SP} Workshop on},
	author = {Sidiropoulos, {N.D.} and Giannakis, {G.B.} and Bro, R.},
	year = {1998},
	pages = {304--307}
},

@article{amari_adaptive_2000,
	title = {Adaptive method of realizing natural gradient learning for multilayer perceptrons},
	volume = {12},
	number = {6},
	journal = {Neural Computation},
	author = {Amari, {S.I.} and Park, H. and Fukumizu, K.},
	year = {2000},
	pages = {1399--1409}
},

@inproceedings{douglas_relationship_1997,
	title = {On the relationship between blind deconvolution and blind source separation},
	volume = {2},
	booktitle = {Signals, Systems \& Computers, 1997. Conference Record of the {Thirty-First} Asilomar Conference on},
	author = {Douglas, {S.C.} and Haykin, S.},
	year = {1997},
	pages = {1591--1595}
},

@book{papoulis_probability_1991,
	edition = {3rd edition},
	title = {Probability, Random Variables, and Stochastic Processes},
	isbn = {0070484775},
	publisher = {{McGraw} Hill Higher Education},
	author = {Papoulis, Athanansios},
	month = mar,
	year = {1991}
},

@inproceedings{signal_image_1990,
	title = {Image Processing},
	volume = {1},
	booktitle = {Proc. Int'l Optical Computing Conf},
	author = {Signal, {J.S.L.T.D.}},
	year = {1990},
	pages = {1977}
},

@article{zibulevsky_blind_2001,
	title = {Blind source separation by sparse decomposition in a signal dictionary},
	volume = {13},
	number = {4},
	journal = {Neural computation},
	author = {Zibulevsky, M. and Pearlmutter, {B.A.}},
	year = {2001},
	pages = {863--882}
},

@article{weinstein_multi-channel_1993,
	title = {Multi-channel signal separation by decorrelation},
	volume = {1},
	number = {4},
	journal = {Speech and Audio Processing, {IEEE} Transactions on},
	author = {Weinstein, E. and Feder, M. and Oppenheim, {A.V.}},
	year = {1993},
	pages = {405--413}
},

@inproceedings{zhang_distinguishing_2008,
	title = {Distinguishing causes from effects using nonlinear acyclic causal models},
	booktitle = {{NIPS} 2008 Workshop on Causality. {URL} http://www. cs. helsinki. {fi/u/ahyvarin/papers/Zhang09NIPSworkshop.} pdf},
	author = {Zhang, K. and Hyv\"arinen, A.},
	year = {2008}
},

@article{dragoman_wigner_1996,
	title = {Wigner distribution function in nonlinear optics.},
	volume = {35},
	doi = {10.1364/AO.35.004142},
	number = {21},
	journal = {Applied Optics},
	author = {Dragoman},
	year = {1996},
	pages = {4142--4146}
},

@article{praly_asymptotic_2003,
	title = {Asymptotic stabilization via output feedback for lower triangular systems with output dependent incremental rate},
	volume = {48},
	number = {6},
	journal = {Automatic Control, {IEEE} Transactions on},
	author = {Praly, L.},
	year = {2003},
	pages = {1103--1108}
},

@article{tong_indeterminacy_1991,
	title = {Indeterminacy and identifiability of blind identification},
	volume = {38},
	number = {5},
	journal = {Circuits and Systems, {IEEE} Transactions on},
	author = {Tong, L. and Liu, {R.W.} and Soon, {V.C.} and Huang, {Y.F.}},
	year = {1991},
	pages = {499--509}
},

@inproceedings{salerno_source_2004,
	title = {Source separation techniques applied to astrophysical maps},
	booktitle = {{Knowledge-Based} Intelligent Information and Engineering Systems},
	author = {Salerno, E. and Tonazzini, A. and Kuruo\u{g}lu, E. and Bedini, L. and Herranz, D. and Baccigalupi, C.},
	year = {2004},
	pages = {426--432}
},

@article{ziehe_blind_2003,
	title = {Blind separation of post-nonlinear mixtures using linearizing transformations and temporal decorrelation},
	volume = {4},
	journal = {The Journal of Machine Learning Research},
	author = {Ziehe, A. and Kawanabe, M. and Harmeling, S. and M\"uller, {K.R.}},
	year = {2003},
	pages = {1319--1338}
},

@article{stone_blind_2001,
	title = {Blind source separation using temporal predictability},
	volume = {13},
	number = {7},
	journal = {Neural computation},
	author = {Stone, {J.V.}},
	year = {2001},
	pages = {1559--1574}
},

@article{mendlovic_graded-index_1994,
	title = {Graded-index fibers, Wigner-distribution functions, and the fractional Fourier transform},
	volume = {33},
	number = {26},
	journal = {Applied optics},
	author = {Mendlovic, D. and Ozaktas, {H.M.} and Lohmann, {A.W.}},
	year = {1994},
	pages = {6188--6193}
},

@article{pajunen_nonlinear_1996,
	title = {Nonlinear Independent Component Analysis by {Self-Organizing} Maps},
	volume = {12},
	url = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.55.2938},
	journal = {{NEURAL} {NETWORKS}},
	author = {Pajunen, Petteri},
	year = {1996},
	pages = {429---439}
},

@book{widrow_adaptive_1996,
	address = {Upper Saddle River, {NJ}, {USA}},
	title = {Adaptive inverse control},
	isbn = {0-13-005968-4},
	publisher = {Prentice Hall Press},
	editor = {Widrow, Bernard and Walach, Eugene},
	year = {1996}
},

@article{abrard_time-frequency_2005,
	title = {A time-frequency blind signal separation method applicable to underdetermined mixtures of dependent sources},
	volume = {85},
	number = {7},
	journal = {Signal Processing},
	author = {Abrard, F. and Deville, Y.},
	year = {2005},
	pages = {1389--1403}
},

@book{hyvarinen_independent_2001-3,
	title = {Independent component analysis},
	volume = {26},
	publisher = {Wiley-interscience},
	author = {Hyv\"arinen, A. and Karhunen, J. and Oja, E.},
	year = {2001}
},

@inproceedings{zhang_natural_1999,
	title = {Natural gradient approach to blind separation of over-and under-complete mixtures},
	booktitle = {In Proceeding of Independent Component Analysis and Signal Separation {(ICA'99}},
	author = {Zhang, {L.Q.} and Amari, S. and Cichocki, A.},
	year = {1999}
},

@article{gorokhov_subspace-based_1997,
	title = {Subspace-based techniques for blind separation of convolutive mixtures with temporally correlated sources},
	volume = {44},
	number = {9},
	journal = {Circuits and Systems I: Fundamental Theory and Applications, {IEEE} Transactions on},
	author = {Gorokhov, A. and Loubaton, P.},
	year = {1997},
	pages = {813--820}
},

@article{cichocki_neural_1999,
	title = {Neural networks for blind separation with unknown number of sources},
	volume = {24},
	number = {1},
	journal = {Neurocomputing},
	author = {Cichocki, A. and Karhunen, J. and Kasprzak, W. and Vigario, R.},
	year = {1999},
	pages = {55--93}
},

@article{chang_texture_1993,
	title = {Texture analysis and classification with tree-structured wavelet transform},
	volume = {2},
	number = {4},
	journal = {Image Processing, {IEEE} Transactions on},
	author = {Chang, T. and Kuo, {C.C.J.}},
	year = {1993},
	pages = {429--441}
},

@article{zacklad_ordre_2008,
	title = {Ordre du discours : cadrage et recadrage des transactions communicationnelles dans les processus de changement et d ' innovation au sein des organisations},
	shorttitle = {Ordre du discours},
	number = {1999},
	journal = {No\^us},
	author = {Zacklad, Manuel and Delaunay, Tech-cico Institut Charles},
	year = {2008},
	pages = {21--22}
},

@inproceedings{tong_new_1991,
	title = {A new approach to blind identification and equalization of multipath channels},
	booktitle = {Signals, Systems and Computers, 1991. 1991 Conference Record of the {Twenty-Fifth} Asilomar Conference on},
	author = {Tong, L. and Xu, G. and Kailath, T.},
	year = {1991},
	pages = {856--860}
},

@article{belouchrani_blind_1997,
	title = {A blind source separation technique using second-order statistics},
	volume = {45},
	number = {2},
	journal = {Signal Processing, {IEEE} Transactions on},
	author = {Belouchrani, A. and {Abed-Meraim}, K. and Cardoso, {J.F.} and Moulines, E.},
	year = {1997},
	pages = {434--444}
},

@article{kopriva_wavelet_2008,
	title = {Wavelet packets approach to blind separation of statistically dependent sources},
	volume = {71},
	number = {7-9},
	journal = {Neurocomputing},
	author = {Kopriva, I. and Sersic, D.},
	year = {2008},
	pages = {1642--1655}
},

@inproceedings{belouchrani_second-order_1993,
	title = {Second-order blind separation of temporally correlated sources},
	booktitle = {Proc. Int. Conf. Digital Signal Processing},
	author = {Belouchrani, A. and {Abed-Meraim}, K. and Cardoso, {JF} and Moulines, E.},
	year = {1993},
	pages = {346--351}
},

@article{ziehe_artifact_2000,
	title = {Artifact reduction in magnetoneurography based on time-delayed second-order correlations},
	volume = {47},
	issn = {0018-9294},
	doi = {10.1109/10.817622},
	abstract = {Artifacts in magnetoneurography data due to endogenous biological noise sources, like the cardiac signal, can be four orders of magnitude higher than the signal of interest. Therefore, it is important to establish effective artifact reduction methods. We propose a blind source separation algorithm using only second-order temporal correlations for cleaning biomagnetic measurements of evoked responses in the peripheral nervous system. The algorithm showed its efficiency by eliminating disturbances originating from biological and technical noise sources and successfully extracting the signal of interest. This yields a significant improvement of the neuro-magnetic source analysis},
	number = {1},
	journal = {{IEEE} Transactions on Biomedical Engineering},
	author = {Ziehe, A. and Muller, K. {-R} and Nolte, G. and Mackert, B. {-M} and Curio, G.},
	month = jan,
	year = {2000},
	keywords = {adaptive signal processing, artifact reduction, Biological information theory, biomagnetic measurements, Biomagnetics, biomagnetism, Biomedical imaging, Biomedical measurements, Blind source separation, blind source separation algorithm, cardiac signal, decorrelation, endogenous biological noise sources, evoked responses, Higher order statistics, higher-order moments, Independent component analysis, interference suppression, linear algebra, Magnetic analysis, Magnetic separation, magnetoneurography, medical signal processing, Nervous system, neuro-magnetic source analysis, neurophysiology, peripheral nervous system, second-order temporal correlations, Signal processing algorithms, time-delayed second-order correlations},
	pages = {75--87}
},

@article{back_-line_1997,
	title = {{ON-LINE} {ADAPTIVE} {ALGORITHMS} {IN} {NON-STATIONARY} {ENVIRONMENTS} {USING} A {MODIFIED} {CONJUGATE} {GRADIENT} {APPROACH}},
	journal = {{NEURAL} {NETWORKS} {FOR} {SIGNAL} {PROCESSING} {VII}},
	author = {{BACK}, {A.C.B.O.A.} and {AMARI}, S.},
	year = {1997},
	pages = {316}
},

@article{barlow_unsupervised_1989,
	title = {Unsupervised learning},
	volume = {1},
	number = {3},
	journal = {Neural computation},
	author = {Barlow, {H.B.}},
	year = {1989},
	pages = {295--311}
},

@inproceedings{peters_reinforcement_2003,
	title = {Reinforcement learning for humanoid robotics},
	booktitle = {Proceedings of the third {IEEE-RAS} international conference on humanoid robots},
	author = {Peters, J. and Vijayakumar, S. and Schaal, S.},
	year = {2003},
	pages = {1--20}
},

@misc{_degenerate_????,
	title = {Degenerate Unmixing Estimation Technique for Undetermined Blind Signal Separation},
	url = {http://www.vocal.com/blind_signal_separation/underdetermined.html},
	howpublished = {http://www.vocal.com/blind\_signal\_separation/underdetermined.html}
},

@article{esposito_independent_2005,
	title = {Independent component analysis of {fMRI} group studies by self-organizing clustering},
	volume = {25},
	number = {1},
	journal = {Neuroimage},
	author = {Esposito, F. and Scarabino, T. and Hyvarinen, A. and Himberg, J. and Formisano, E. and Comani, S. and Tedeschi, G. and Goebel, R. and Seifritz, E. and Di Salle, F.},
	year = {2005},
	pages = {193--205}
},

@inproceedings{buchner_trinicon:_2004,
	title = {{TRINICON:} A versatile framework for multichannel blind signal processing},
	volume = {3},
	shorttitle = {{TRINICON}},
	booktitle = {Acoustics, Speech, and Signal Processing, 2004. {Proceedings.(ICASSP'04).} {IEEE} International Conference on},
	author = {Buchner, H. and Aichner, R. and Kellermann, W.},
	year = {2004},
	pages = {iii--889}
},

@inproceedings{lee_blind_1997-2,
	title = {Blind source separation of real world signals},
	volume = {4},
	booktitle = {Neural Networks, 1997., International Conference on},
	author = {Lee, {T.W.} and Bell, {A.J.} and Orglmeister, R.},
	year = {1997},
	pages = {2129--2134}
},

@article{engheta_electrostatic_1996,
	title = {Electrostatic ``fractional'' image methods for perfectly conducting wedges and cones},
	volume = {44},
	issn = {{0018-926X}},
	doi = {10.1109/8.546242},
	abstract = {Engheta (1996) introduced a definition for the electric charge ``fractional-order'' multipoles using the concept of fractional derivatives and integrals. Here, we utilize that definition to introduce a detailed image theory for the two-dimensional {(2-D)} electrostatic potential distributions in front of a perfectly conducting wedge with arbitrary wedge angles, and for the three-dimensional potential in front of a perfectly conducting cone with arbitrary cone angles. We show that the potentials in the presence of these structures can be described equivalently as the electrostatic potentials of sets of equivalent ``image'' charge distributions that effectively behave as ``fractional-order'' multipoles; hence, the name ``fractional'' image methods. The fractional orders of these so-called fractional images depend on the wedge angle (for the wedge problem) and on the cone angle (for the cone problem). Special cases where these fractional images behave like the discrete images are discussed, and physical justification and insights into these results are given},
	number = {12},
	journal = {{IEEE} Transactions on Antennas and Propagation},
	author = {Engheta, N.},
	month = dec,
	year = {1996},
	keywords = {cone angle, Dielectrics, electric charge, electric charge fractional-order multipoles, electric potential, Electromagnetic fields, electrostatic fractional image methods, Electrostatics, equivalent image charge distributions, Fractional calculus, fractional images, Geometry, image theory, Kelvin, Laplace equations, Material properties, perfectly conducting cones, perfectly conducting wedges, three-dimensional potential, Two dimensional displays, two-dimensional electrostatic potential distributions, wedge angle},
	pages = {1565--1574}
},

@article{karhunen_nonlinear_1998,
	title = {The nonlinear {PCA} criterion in blind source separation: Relations with other approaches},
	volume = {22},
	shorttitle = {The nonlinear {PCA} criterion in blind source separation},
	number = {1-3},
	journal = {Neurocomputing},
	author = {Karhunen, J. and Pajunen, P. and Oja, E.},
	year = {1998},
	pages = {5--20}
},

@book{sejnowski_unsupervised_1999,
	title = {Unsupervised learning: foundations of neural computation},
	shorttitle = {Unsupervised learning},
	publisher = {The {MIT} Press},
	author = {Sejnowski, {T.J.}},
	year = {1999}
},

@article{cincotti_generalized_1992,
	title = {Generalized {self-Fourier} functions},
	volume = {25},
	issn = {0305-4470, 1361-6447},
	url = {http://iopscience.iop.org/0305-4470/25/20/001},
	doi = {10.1088/0305-4470/25/20/001},
	number = {20},
	journal = {Journal of Physics A: Mathematical and General},
	author = {Cincotti, G and Gori, F and Santarsiero, M},
	month = oct,
	year = {1992},
	pages = {L1191--L1194}
},

@inproceedings{tong_waveform-preserving_1991,
	title = {Waveform-preserving blind estimation of multiple sources},
	booktitle = {Decision and Control, 1991., Proceedings of the 30th {IEEE} Conference on},
	author = {Tong, L. and Soon, {VC} and Inouye, Y. and Huang, Y. and Liu, R.},
	year = {1991},
	pages = {2388--2393}
},

@inproceedings{hansen_ica_2003,
	title = {{ICA} if {fMRI} based on a convolutive mixture model},
	booktitle = {Ninth Annual Meeting of the Organization for Human Brain Mapping {(\$\$HBM\$\$} 2003), {NewYork}, 2003 June.},
	author = {Hansen, {L.K.}},
	year = {2003}
},

@article{zhang_semiparametric_2001,
	title = {Semiparametric model and superefficiency in blind deconvolution},
	volume = {81},
	number = {12},
	journal = {Signal processing},
	author = {Zhang, {L.Q.} and Amari, {S.I.} and Cichocki, A.},
	year = {2001},
	pages = {2535--2553}
},

@inproceedings{widrow_nonlinear_1997,
	title = {Nonlinear adaptive inverse control},
	volume = {2},
	booktitle = {Decision and Control, 1997., Proceedings of the 36th {IEEE} Conference on},
	author = {Widrow, B. and Plett, {G.L.}},
	year = {1997},
	pages = {1032--1037}
},

@book{dorf_modern_1998,
	title = {Modern Control Systems: Solutions Manual},
	shorttitle = {Modern Control Systems},
	publisher = {{Addison-Wesley}},
	author = {Dorf, {R.C.} and Bishop, {R.H.}},
	year = {1998}
},

@article{beckmann_probabilistic_2004,
	title = {Probabilistic independent component analysis for functional magnetic resonance imaging},
	volume = {23},
	number = {2},
	journal = {Medical Imaging, {IEEE} Transactions on},
	author = {Beckmann, {C.F.} and Smith, {S.M.}},
	year = {2004},
	pages = {137--152}
},

@article{sigl_introduction_1994,
	title = {An introduction to bispectral analysis for the electroencephalogram},
	volume = {10},
	number = {6},
	journal = {Journal of Clinical Monitoring and Computing},
	author = {Sigl, {J.C.} and Chamoun, {N.G.}},
	year = {1994},
	pages = {392--404}
},

@article{delorme_eeglab:_2004,
	title = {{EEGLAB:} an open source toolbox for analysis of single-trial {EEG} dynamics including independent component analysis},
	volume = {134},
	shorttitle = {{EEGLAB}},
	number = {1},
	journal = {Journal of neuroscience methods},
	author = {Delorme, A. and Makeig, S.},
	year = {2004},
	pages = {9--21}
},

@article{choi_flexible_2000,
	title = {Flexible independent component analysis},
	volume = {26},
	number = {1},
	journal = {The Journal of {VLSI} Signal Processing},
	author = {Choi, S. and Cichocki, A. and Amari, {S.I.}},
	year = {2000},
	pages = {25--38}
},

@article{bofill_underdetermined_2003,
	title = {Underdetermined blind separation of delayed sound sources in the frequency domain},
	volume = {55},
	number = {3},
	journal = {Neurocomputing},
	author = {Bofill, P.},
	year = {2003},
	pages = {627--641}
},

@inproceedings{miskin_ensemble_2000,
	title = {Ensemble learning for independent component analysis},
	booktitle = {in Advances in Independent Component Analysis},
	author = {Miskin, {J.W.}},
	year = {2000}
},

@inproceedings{farah_multispectral_2003,
	title = {Multispectral satellite image analysis based on the method of blind separation and fusion of sources},
	volume = {6},
	booktitle = {Geoscience and Remote Sensing Symposium, 2003. {IGARSS'03.} Proceedings. 2003 {IEEE} International},
	author = {Farah, {IR} and Ahmed, {M.B.} and Boussema, {MR}},
	year = {2003},
	pages = {3638--3640}
},

@article{lu_approach_2005,
	title = {Approach and applications of constrained {ICA}},
	volume = {16},
	number = {1},
	journal = {Neural Networks, {IEEE} Transactions on},
	author = {Lu, W. and Rajapakse, {J.C.}},
	year = {2005},
	pages = {203--212}
},

@article{chi_inverse_1995,
	title = {Inverse filter criteria for blind deconvolution and equalization using two cumulants},
	volume = {43},
	number = {1},
	journal = {Signal Processing},
	author = {Chi, {C.Y.} and Wu, {M.C.}},
	year = {1995},
	pages = {55--63}
},

@article{de_lathauwer_fetal_2000,
	title = {Fetal electrocardiogram extraction by blind source subspace separation},
	volume = {47},
	number = {5},
	journal = {Biomedical Engineering, {IEEE} Transactions on},
	author = {De Lathauwer, L. and De Moor, B. and Vandewalle, J.},
	year = {2000},
	pages = {567--572}
},

@inproceedings{zhang_blind_1998,
	title = {Blind deconvolution/equalization using state-space models},
	booktitle = {Neural Networks for Signal Processing {VIII}, 1998. Proceedings of the 1998 {IEEE} Signal Processing Society Workshop},
	author = {Zhang, L. and Cichocki, A.},
	year = {1998},
	pages = {123--131}
},

@inproceedings{tsoi_blind_2003,
	title = {Blind deconvolution of dynamical systems using a balanced parameterized state space approach},
	volume = {4},
	booktitle = {Acoustics, Speech, and Signal Processing, 2003. {Proceedings.(ICASSP'03).} 2003 {IEEE} International Conference on},
	author = {Tsoi, {AC} and Ma, {LS}},
	year = {2003},
	pages = {IV--309}
},

@article{linsker_local_1992,
	title = {Local Synaptic Learning Rules Suffice to Maximize Mutual Information in a Linear Network},
	volume = {4},
	issn = {0899-7667},
	url = {http://dx.doi.org/10.1162/neco.1992.4.5.691},
	doi = {10.1162/neco.1992.4.5.691},
	abstract = {A network that develops to maximize the mutual information between its output and the signal portion of its input (which is admixed with noise) is useful for extracting salient input features, and may provide a model for aspects of biological neural network function. I describe a local synaptic Learning rule that performs stochastic gradient ascent in this information-theoretic quantity, for the case in which the input-output mapping is linear and the input signal and noise are multivariate gaussian. Feedforward connection strengths are modified by a Hebbian rule during a "learning" phase in which examples of input signal plus noise are presented to the network, and by an {anti-Hebbian} rule during an "unlearning" phase in which examples of noise alone are presented. Each recurrent lateral connection has two values of connection strength, one for each phase; these values are updated by an {anti-Hebbian} rule.},
	number = {5},
	journal = {Neural Computation},
	author = {Linsker, Ralph},
	year = {1992},
	pages = {691--702}
},

@article{courcelle_axiomatisation_1999,
	title = {Une axiomatisation au premier ordre des arrangements de pseudodroites euclidiennes},
	volume = {49},
	number = {3},
	author = {Courcelle, Bruno and Olive, Fr\'ed\'eric},
	year = {1999},
	pages = {883--904}
},

@article{jutten_three_2004,
	title = {Three easy ways for separating nonlinear mixtures?},
	volume = {84},
	number = {2},
	journal = {Signal Processing},
	author = {Jutten, C. and {Babaie-Zadeh}, M. and Hosseini, S.},
	year = {2004},
	pages = {217--229}
},

@book{almeida_nonlinear_2006,
	title = {Nonlinear source separation},
	isbn = {9781598290301},
	abstract = {The purpose of this lecture book is to present the state of the art in nonlinear blind source separation, in a form appropriate for students, researchers and developers. Source separation deals with the problem of recovering sources that are observed in a mixed condition. When we have little knowledge about the sources and about the mixture process, we speak of blind source separation. Linear blind source separation is a relatively well studied subject, however nonlinear blind source separation is still in a less advanced stage, but has seen several significant developments in the last few years. This publication reviews the main nonlinear separation methods, including the separation of post-nonlinear mixtures, and the {MISEP}, ensemble learning and {kTDSEP} methods for generic mixtures. These methods are studied with a significant depth. A historical overview is also presented, mentioning most of the relevant results, on nonlinear blind source separation, that have been presented over the years.},
	publisher = {Morgan \& Claypool Publishers},
	author = {Almeida, Luis B.},
	year = {2006},
	keywords = {Blind source separation, Computers / Data Transmission Systems / General, Nonlinear theories, Technology \& Engineering / Electrical, Technology \& Engineering / Signals \& Signal Processing, Technology \& Engineering / Telecommunications}
},

@article{shalvi_super-exponential_1993,
	title = {Super-exponential methods for blind deconvolution},
	volume = {39},
	number = {2},
	journal = {Information Theory, {IEEE} Transactions on},
	author = {Shalvi, O. and Weinstein, E.},
	year = {1993},
	pages = {504--519}
},

@article{salustri_fetal_2005,
	title = {Fetal magnetocardiographic signals extracted by'signal subspace'blind source separation},
	volume = {52},
	number = {6},
	journal = {Biomedical Engineering, {IEEE} Transactions on},
	author = {Salustri, C. and Barbati, G. and Porcaro, C.},
	year = {2005},
	pages = {1140--1142}
},

@article{naanaa_blind_2005,
	title = {Blind source separation of positive and partially correlated data},
	volume = {85},
	number = {9},
	journal = {Signal processing},
	author = {Naanaa, W. and Nuzillard, {J.M.}},
	year = {2005},
	pages = {1711--1722}
},

@inproceedings{cichocki_application_2000,
	title = {Application of {ICA} for automatic noise and interference cancellation in multisensory biomedical signals},
	booktitle = {Proceedings of the Second International Workshop on {ICA} and {BSS}, {ICA}},
	author = {Cichocki, A. and Vorobyov, S.},
	year = {2000},
	pages = {621--626}
},

@article{a_neural_1999,
	title = {Neural networks for blind separation with unknown number of sources - An adaptive algorithm based on neuromimetic architecture},
	volume = {24},
	doi = {10.1016/S0925-2312(98)00091-5},
	number = {1},
	journal = {Neurocomputing},
	author = {A, Cichocki and J, Karhunen and W, Kasprzak and R, Vigario},
	year = {1999},
	keywords = {blind separation, image processing, Neural networks, Signal Reconstruction, Unsupervised learning},
	pages = {55--93}
},

@article{hyvarinen_fast_1999,
	title = {Fast and robust fixed-point algorithms for independent component analysis},
	volume = {10},
	number = {3},
	journal = {Neural Networks, {IEEE} Transactions on},
	author = {Hyvarinen, A.},
	year = {1999},
	pages = {626--634}
},

@article{principe_gamma_1993,
	title = {{THE} {GAMMA} {FILTER} - A New Class of Adaptive {IIR} Filters with Restricted Feedback},
	volume = {41},
	url = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.29.4038},
	journal = {{IEEE} {TRANSACTIONS} {ON} {SIGNAL} {PROCESSING}},
	author = {Principe, Jose C and De Vries, Bert and De Oliveira, Pedro Guedes},
	year = {1993},
	pages = {649---656}
},

@article{yeredor_non-orthogonal_2002,
	title = {Non-orthogonal joint diagonalization in the least-squares sense with application in blind source separation},
	volume = {50},
	number = {7},
	journal = {Signal Processing, {IEEE} Transactions on},
	author = {Yeredor, A.},
	year = {2002},
	pages = {1545--1553}
},

@article{dragoman_i:_1997,
	title = {I: The Wigner Distribution Function in Optics and Optoelectronics},
	volume = {37},
	shorttitle = {I},
	journal = {Progress in Optics},
	author = {Dragoman},
	year = {1997},
	pages = {1--56}
},

@article{roan_new_2002,
	title = {A new, non-linear, adaptive, blind source separation approach to gear tooth failure detection and analysis},
	volume = {16},
	number = {5},
	journal = {Mechanical Systems and Signal Processing},
	author = {Roan, {MJ} and Erling, {JG} and Sibul, {LH}},
	year = {2002},
	pages = {719--740}
},

@article{hyvarinen_fixed-point_1999,
	title = {The fixed-point algorithm and maximum likelihood estimation for independent component analysis},
	volume = {10},
	number = {1},
	journal = {Neural Processing Letters},
	author = {Hyv\"arinen, A.},
	year = {1999},
	pages = {1--5}
},

@article{torkkola_motorola_????,
	title = {Motorola, Phoenix Corporate Research Labs 2100 East Elliot Road, {MD} {EL508}, Tempe, {AZ} 85284, {USA}},
	author = {Torkkola, K.}
},

@article{hovakimyan_novel_2002,
	title = {A novel error observer-based adaptive output feedback approach for control of uncertain systems},
	volume = {47},
	number = {8},
	journal = {Automatic Control, {IEEE} Transactions on},
	author = {Hovakimyan, N. and Nardi, F. and Calise, {A.J.}},
	year = {2002},
	pages = {1310--1314}
},

@inproceedings{vielva_underdetermined_2001,
	title = {Underdetermined blind source separation using a probabilistic source sparsity model},
	volume = {2001},
	booktitle = {Proc. {ICA}},
	author = {Vielva, L. and Erdogmus, D. and Principe, {J.C.}},
	year = {2001},
	pages = {675--679}
},

@inproceedings{hyvarinen_fast_1999-1,
	title = {Fast {ICA} for noisy data using Gaussian moments},
	volume = {5},
	booktitle = {Circuits and Systems, 1999. {ISCAS'99.} Proceedings of the 1999 {IEEE} International Symposium on},
	author = {Hyvarinen, A.},
	year = {1999},
	pages = {57--61}
},

@article{hyvarinen_independent_1998-1,
	title = {Independent component analysis in the presence of Gaussian noise by maximizing joint likelihood},
	volume = {22},
	number = {1-3},
	journal = {Neurocomputing},
	author = {Hyv\"arinen, A.},
	year = {1998},
	pages = {49--67}
},

@article{lee_independent_1999,
	title = {Independent component analysis using an extended infomax algorithm for mixed subgaussian and supergaussian sources},
	volume = {11},
	number = {2},
	journal = {Neural computation},
	author = {Lee, {T.W.} and Girolami, M. and Sejnowski, {T.J.}},
	year = {1999},
	pages = {417--441}
},

@article{pedersen_survey_2007,
	title = {A survey of convolutive blind source separation methods},
	journal = {Multichannel Speech Processing Handbook},
	author = {Pedersen, {M.S.} and Larsen, J. and Kjems, U. and Parra, {L.C.}},
	year = {2007}
},

@inproceedings{choi_blind_1997,
	title = {Blind signal deconvolution by spatio-temporal decorrelation and demixing},
	booktitle = {Neural Networks for Signal Processing [1997] {VII.} Proceedings of the 1997 {IEEE} Workshop},
	author = {Choi, S. and Cichocki, A.},
	year = {1997},
	keywords = {{Anti-Hebbian} learning, blind equalization, Blind equalizers, blind signal deconvolution, Blind source separation, Deconvolution, decorrelation, Delay, demixing, Filters, i.i.d. sources, inverse filter, local unsupervised learning algorithm, polynomial matrices, Signal processing algorithms, signal resolution, signal sources, Source separation, spatio-temporal decorrelation, Time domain analysis, two-stage neural network, Unsupervised learning, Vectors},
	pages = {426--435}
},

@article{ozaktas_fractional_1995,
	title = {Fractional Fourier optics},
	volume = {12},
	number = {4},
	journal = {{JOSA} A},
	author = {Ozaktas, {H.M.} and Mendlovic, D.},
	year = {1995},
	pages = {743--751}
},

@article{gribonval_sur_2007,
	title = {Sur quelques probl\`emes math\'ematiques de mod{\textbackslash}textbackslash'elisation parcimonieuse},
	author = {Gribonval, R.},
	year = {2007}
},

@article{liu_sequential_1998,
	title = {Sequential Monte Carlo methods for dynamic systems},
	journal = {Journal of the American statistical association},
	author = {Liu, {J.S.} and Chen, R.},
	year = {1998},
	pages = {1032--1044}
},

@inproceedings{jourjine_blind_2000,
	title = {Blind separation of disjoint orthogonal signals: Demixing n sources from 2 mixtures},
	volume = {5},
	shorttitle = {Blind separation of disjoint orthogonal signals},
	booktitle = {Acoustics, Speech, and Signal Processing, 2000. {ICASSP'00.} Proceedings. 2000 {IEEE} International Conference on},
	author = {Jourjine, A. and Rickard, S. and Yilmaz, O.},
	year = {2000},
	pages = {2985--2988}
},

@article{himberg_validating_2004,
	title = {Validating the independent components of neuroimaging time series via clustering and visualization},
	volume = {22},
	number = {3},
	journal = {Neuroimage},
	author = {Himberg, J. and Hyvarinen, A. and Esposito, F.},
	year = {2004},
	pages = {1214--1222}
},

@article{tong_blind_1994,
	title = {Blind identification and equalization based on second-order statistics: A time domain approach},
	volume = {40},
	shorttitle = {Blind identification and equalization based on second-order statistics},
	number = {2},
	journal = {Information Theory, {IEEE} Transactions on},
	author = {Tong, L. and Xu, G. and Kailath, T.},
	year = {1994},
	pages = {340--349}
},

@article{alieva_radon-wigner_1999,
	title = {{Radon-Wigner} transform for optical field analysis},
	volume = {98},
	journal = {Optics and Optoelectronics, Theory, Devices and Applications, Proc. {ICOL}},
	author = {Alieva, T. and Bastiaans, {M.J.}},
	year = {1999},
	pages = {132--135}
},

@book{aldous_fourier_1985,
	edition = {1},
	title = {Fourier Series and Integrals},
	isbn = {0122264517},
	publisher = {Academic Press},
	author = {Aldous, David and Tong, Y. L.},
	editor = {Dym, H. and {McKean}, H. P.},
	month = oct,
	year = {1985}
},

@book{dudgeon_multidimensional_1983,
	title = {Multidimensional Digital Signal Processing},
	isbn = {0136049591},
	publisher = {Prentice Hall},
	author = {Dudgeon, Dan E. and Mersereau, Russell M.},
	month = sep,
	year = {1983}
},

@article{bobin_sparsity_2007,
	title = {Sparsity and morphological diversity in blind source separation},
	volume = {16},
	number = {11},
	journal = {Image Processing, {IEEE} Transactions on},
	author = {Bobin, J. and Starck, {J.L.} and Fadili, J. and Moudden, Y.},
	year = {2007},
	pages = {2662--2674}
},

@article{zhang_multichannel_2004,
	title = {Multichannel blind deconvolution of nonminimum-phase systems using filter decomposition},
	volume = {52},
	number = {5},
	journal = {Signal Processing, {IEEE} Transactions on},
	author = {Zhang, L. and Cichocki, A. and Amari, S.},
	year = {2004},
	pages = {1430--1442}
},

@article{olshausen_sparse_1997,
	title = {Sparse coding with an overcomplete basis set: A strategy employed by V1?},
	volume = {37},
	shorttitle = {Sparse coding with an overcomplete basis set},
	number = {23},
	journal = {Vision research},
	author = {Olshausen, {B.A.} and Field, {D.J.}},
	year = {1997},
	pages = {3311--3325}
},

@article{bastiaans_wigner_1932,
	title = {Wigner Distribution in Optics},
	journal = {Distribution},
	author = {Bastiaans, Martin J},
	year = {1932},
	pages = {1--42}
},

@article{amari_natural_1998,
	title = {Natural gradient works efficiently in learning},
	volume = {10},
	number = {2},
	journal = {Neural computation},
	author = {Amari, {S.I.}},
	year = {1998},
	pages = {251--276}
},

@article{torrence_practical_1998,
	title = {A practical guide to wavelet analysis},
	volume = {79},
	number = {1},
	journal = {Bulletin of the American Meteorological Society},
	author = {Torrence, C. and Compo, {G.P.}},
	year = {1998},
	pages = {61--78}
},

@article{correa_performance_2007,
	title = {Performance of blind source separation algorithms for {fMRI} analysis using a group {ICA} method},
	volume = {25},
	number = {5},
	journal = {Magnetic resonance imaging},
	author = {Correa, N. and Adali, T. and Calhoun, {V.D.}},
	year = {2007},
	pages = {684--694}
},

@inproceedings{yang_performance_2004,
	title = {Performance of variable step-size {LMS} algorithms for linear adaptive inverse control systems},
	booktitle = {Engineering, Sciences and Technology, Student Conference On},
	author = {Yang, T.},
	year = {2004},
	pages = {122--126}
},

@article{cao_robust_2003,
	title = {A robust approach to independent component analysis of signals with high-level noise measurements},
	volume = {14},
	number = {3},
	journal = {Neural Networks, {IEEE} Transactions on},
	author = {Cao, J. and Murata, N. and Amari, S. and Cichocki, A. and Takeda, T.},
	year = {2003},
	pages = {631--645}
},

@article{girolami_variational_2001,
	title = {A variational method for learning sparse and overcomplete representations},
	volume = {13},
	number = {11},
	journal = {Neural computation},
	author = {Girolami, M.},
	year = {2001},
	pages = {2517--2532}
},

@article{amari_natural_1999-1,
	title = {Natural Gradient Learning for Over- and {Under-Complete} Bases in {ICA}},
	volume = {11},
	issn = {0899-7667},
	url = {http://dx.doi.org/10.1162/089976699300015990},
	doi = {10.1162/089976699300015990},
	abstract = {Independent component analysis or blind source separation is a new technique of extracting independent signals from mixtures. It is applicable even when the number of independent sources is unknown and is larger or smaller than the number of observed mixture signals. This article extends the natural gradient learning algorithm to be applicable to these overcomplete and undercomplete cases. Here, the observed signals are assumed to be whitened by preprocessing, so that we use the natural Riemannian gradient in Stiefel manifolds.},
	number = {8},
	journal = {Neural Computation},
	author = {Amari, Shun-ichi},
	year = {1999},
	pages = {1875--1883}
},

@article{igual_informed_2007,
	title = {An informed source separation of astrophysical ice analogs},
	volume = {17},
	number = {5},
	journal = {Digital Signal Processing},
	author = {Igual, J. and Llinares, R.},
	year = {2007},
	pages = {947--964}
},

@article{stockwell_localization_1996,
	title = {Localization of the complex spectrum: the S transform},
	volume = {44},
	shorttitle = {Localization of the complex spectrum},
	number = {4},
	journal = {Signal Processing, {IEEE} Transactions on},
	author = {Stockwell, {RG} and Mansinha, L. and Lowe, {RP}},
	year = {1996},
	pages = {998--1001}
},

@misc{_introduction_????,
	title = {Introduction to Blind Source Separation},
	url = {http://perso.univ-rennes1.fr/laurent.albera/alberasiteweb/bss.html},
	howpublished = {http://perso.univ-rennes1.fr/laurent.albera/alberasiteweb/bss.html}
},

@article{offset_nonlinear_????,
	title = {The Nonlinear {PCA} Learning Rule and Signal {Separation-Mathematical} Analysis},
	author = {{OFFSET}, {TKK}}
},

@article{dorsch_chirp_1994,
	title = {Chirp filtering in the fractional Fourier domain},
	volume = {33},
	number = {32},
	journal = {Applied optics},
	author = {Dorsch, {R.G.} and Lohmann, {A.W.} and Bitran, Y. and Mendlovic, D. and Ozaktas, {H.M.}},
	year = {1994},
	pages = {7599--7602}
},

@article{scholkopf_nonlinear_1998,
	title = {Nonlinear component analysis as a kernel eigenvalue problem},
	volume = {10},
	number = {5},
	journal = {Neural computation},
	author = {Sch\"olkopf, B. and Smola, A. and M\"uller, {K.R.}},
	year = {1998},
	pages = {1299--1319}
},

@article{parra_blind_2003,
	title = {Blind source separation via generalized eigenvalue decomposition},
	volume = {4},
	journal = {The Journal of Machine Learning Research},
	author = {Parra, L. and Sajda, P.},
	year = {2003},
	pages = {1261--1269}
},

@article{zhang_adaptive_2002,
	title = {Adaptive observer for multiple-input-multiple-output {(MIMO)} linear time-varying systems},
	volume = {47},
	number = {3},
	journal = {Automatic Control, {IEEE} Transactions on},
	author = {Zhang, Q.},
	year = {2002},
	pages = {525--529}
},

@article{bofill_underdetermined_2001,
	title = {Underdetermined blind source separation using sparse representations},
	volume = {81},
	number = {11},
	journal = {Signal processing},
	author = {Bofill, P. and Zibulevsky, M.},
	year = {2001},
	pages = {2353--2362}
},

@article{moussaoui_separation_2005,
	title = {S\'eparation de sources non-n\'egatives. Application au traitement des signaux de spectroscopie},
	author = {Moussaoui},
	year = {2005}
},

@article{smith_advances_2004,
	title = {Advances in functional and structural {MR} image analysis and implementation as {FSL}},
	volume = {23},
	journal = {Neuroimage},
	author = {Smith, {S.M.} and Jenkinson, M. and Woolrich, {M.W.} and Beckmann, {C.F.} and Behrens, {T.E.J.} and {Johansen-Berg}, H. and Bannister, {P.R.} and De Luca, M. and Drobnjak, I. and Flitney, {D.E.} and others},
	year = {2004},
	pages = {S208--S219}
},

@article{hyvarinen_blind_2001,
	title = {Blind source separation by nonstationarity of variance: A cumulant-based approach},
	volume = {12},
	shorttitle = {Blind source separation by nonstationarity of variance},
	number = {6},
	journal = {Neural Networks, {IEEE} Transactions on},
	author = {Hyvarinen, A.},
	year = {2001},
	pages = {1471--1474}
},

@article{schetinin_4_2007,
	title = {4 Advanced Feature Recognition and Classification Using Artificial Intelligence Paradigms},
	journal = {Artificial Intelligence in Recognition and Classification of Astrophysical and Medical Images},
	author = {Schetinin, V. and Zharkova, V. and Brazhnikov, A. and Zharkov, S. and Salerno, E. and Bedini, L. and Kuruoglu, E. and Tonazzini, A. and Zazula, D. and Cigale, B. and others},
	year = {2007},
	pages = {151--338}
},

@inproceedings{amari_novel_1997,
	title = {Novel on-line adaptive learning algorithms for blind deconvolution using the natural gradient approach},
	booktitle = {in Proc. {SYSID}, Kitakyushu},
	author = {Amari, S. and Douglas, {S.C.} and Cichocki, A. and Yang, {H.H.}},
	year = {1997}
},

@article{bedini_separation_2005,
	title = {Separation of correlated astrophysical sources using multiple-lag data covariance matrices},
	volume = {2005},
	journal = {{EURASIP} journal on applied signal processing},
	author = {Bedini, L. and Herranz, D. and Salerno, E. and Baccigalupi, C. and Kuruo\v{g}lu, {EE} and Tonazzini, A.},
	year = {2005},
	pages = {2400--2412}
},

@article{belouchrani_blind_1998,
	title = {Blind source separation based on time-frequency signal representations},
	volume = {46},
	number = {11},
	journal = {Signal Processing, {IEEE} Transactions on},
	author = {Belouchrani, A. and Amin, {M.G.}},
	year = {1998},
	pages = {2888--2897}
},

@article{yeredor_blind_2000,
	title = {Blind source separation via the second characteristic function},
	volume = {80},
	number = {5},
	journal = {Signal Processing},
	author = {Yeredor, A.},
	year = {2000},
	pages = {897--902}
},

@article{cichocki_adaptive_1996,
	title = {Adaptive Approach To Blind Source Separation With Cancellation Of Additive And Convolutional Noise},
	url = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.48.9466},
	journal = {{IN} {INTERNATIONAL} {CONFERENCE} {ON} {SIGNAL} {PROCESSING}},
	author = {Cichocki, A. and Kasprzak, W. and Amari, S. {-I}},
	year = {1996},
	pages = {412---415}
},

@article{choi_second_2002,
	title = {Second order nonstationary source separation},
	volume = {32},
	number = {1},
	journal = {The Journal of {VLSI} Signal Processing},
	author = {Choi, S. and Cichocki, A. and Beloucharni, A.},
	year = {2002},
	pages = {93--104}
},

@article{taware_design_2002,
	title = {Design and analysis of a hybrid control scheme for sandwich nonsmooth nonlinear systems},
	volume = {47},
	number = {1},
	journal = {Automatic Control, {IEEE} Transactions on},
	author = {Taware, A. and Tao, G. and Teolis, C.},
	year = {2002},
	pages = {145--150}
},

@article{esposito_real-time_2003,
	title = {Real-time independent component analysis of {fMRI} time-series},
	volume = {20},
	number = {4},
	journal = {{NeuroImage}},
	author = {Esposito, F. and Seifritz, E. and Formisano, E. and Morrone, R. and Scarabino, T. and Tedeschi, G. and Cirillo, S. and Goebel, R. and Di Salle, F.},
	year = {2003},
	pages = {2209--2224}
},

@article{sanchez_frontiers_2002,
	title = {Frontiers of research in {BSS/ICA}},
	volume = {49},
	number = {1},
	journal = {Neurocomputing},
	author = {S\'anchez, A. and David, V.},
	year = {2002},
	pages = {7--23}
},

@article{oja_signal_1995,
	title = {Signal separation by nonlinear Hebbian learning},
	journal = {Computational intelligence: A dynamic system perspective},
	author = {Oja, E. and Karhunen, J.},
	year = {1995},
	pages = {83--97}
},

@article{bach_kernel_2003,
	title = {Kernel independent component analysis},
	volume = {3},
	journal = {The Journal of Machine Learning Research},
	author = {Bach, {F.R.} and Jordan, {M.I.}},
	year = {2003},
	pages = {1--48}
},

@inproceedings{joho_joint_2002,
	title = {Joint diagonalization of correlation matrices by using gradient methods with application to blind signal separation},
	booktitle = {Sensor Array and Multichannel Signal Processing Workshop Proceedings, 2002},
	author = {Joho, M. and Mathis, H.},
	year = {2002},
	pages = {273--277}
},

@article{lee_unifying_2000,
	title = {A unifying information-theoretic framework for independent component analysis},
	volume = {39},
	number = {11},
	journal = {Computers \& Mathematics with Applications},
	author = {Lee, {T.W.} and Girolami, M. and Bell, {A.J.} and Sejnowski, {T.J.}},
	year = {2000},
	pages = {1--21}
},

@article{belouchrani_separation_1993,
	title = {S\'eparation aveugle au second ordre de sources corr\'el\'ees},
	journal = {{GRETSI} Groupe {d'Etudes} du Traitement du Signal et des Images},
	author = {Belouchrani and Abed-meraim, Karim},
	year = {1993}
},

@book{oja_nonlinear_1995,
	title = {The nonlinear {PCA} learning rule and signal separation: Mathematical analysis},
	shorttitle = {The nonlinear {PCA} learning rule and signal separation},
	publisher = {Helsinki University of Technology},
	author = {Oja, E.},
	year = {1995}
},

@article{douglas_natural_2005,
	title = {Natural gradient multichannel blind deconvolution and speech separation using causal {FIR} filters},
	volume = {13},
	number = {1},
	journal = {Speech and Audio Processing, {IEEE} Transactions on},
	author = {Douglas, {S.C.} and Sawada, H. and Makino, S.},
	year = {2005},
	pages = {92--104}
},

@article{de_lathauwer_dimensionality_2004,
	title = {Dimensionality reduction in higher-order signal processing and {rank-(R1}, R2,..., {RN)} reduction in multilinear algebra},
	volume = {391},
	journal = {Linear Algebra and its Applications},
	author = {De Lathauwer, L. and Vandewalle, J.},
	year = {2004},
	pages = {31--55}
},

@article{pham_blind_2001-1,
	title = {Blind separation of instantaneous mixtures of nonstationary sources},
	volume = {49},
	number = {9},
	journal = {Signal Processing, {IEEE} Transactions on},
	author = {Pham, {D.T.} and Cardoso, {J.F.}},
	year = {2001},
	pages = {1837--1848}
},

@article{cao_general_1996,
	title = {General approach to blind source separation},
	volume = {44},
	number = {3},
	journal = {Signal Processing, {IEEE} Transactions on},
	author = {Cao, {X.R.} and Liu, R.},
	year = {1996},
	pages = {562--571}
},

@article{mendlovic_fractional_1995,
	title = {Fractional correlation},
	volume = {34},
	number = {2},
	journal = {Applied optics},
	author = {Mendlovic, D. and Ozaktas, {H.M.} and Lohmann, {A.W.}},
	year = {1995},
	pages = {303--309}
},

@article{tugnait_comments_1992,
	title = {Comments {onNew} criteria for blind deconvolution of nonminimum phase systems (channels)'[and reply]},
	volume = {38},
	number = {1},
	journal = {Information Theory, {IEEE} Transactions on},
	author = {Tugnait, {J.K.} and Shalvi, O. and Weinstein, E.},
	year = {1992},
	pages = {210--213}
},

@article{su_adaptive_2005,
	title = {Adaptive variable structure control of a class of nonlinear systems with unknown {Prandtl-Ishlinskii} hysteresis},
	volume = {50},
	number = {12},
	journal = {Automatic Control, {IEEE} Transactions on},
	author = {Su, {C.Y.} and Wang, Q. and Chen, X. and Rakheja, S.},
	year = {2005},
	pages = {2069--2074}
},

@article{liu_recent_1996,
	title = {Recent developments in blind channel equalization: From cyclostationarity to subspaces},
	volume = {50},
	shorttitle = {Recent developments in blind channel equalization},
	number = {1-2},
	journal = {Signal Processing},
	author = {Liu, H. and Xu, G. and Tong, L. and Kailath, T.},
	year = {1996},
	pages = {83--99}
},

@article{stone_spatiotemporal_2002,
	title = {Spatiotemporal independent component analysis of event-related {fMRI} data using skewed probability density functions},
	volume = {15},
	number = {2},
	journal = {{NeuroImage}},
	author = {Stone, {JV} and Porrill, J. and Porter, {NR} and Wilkinson, {ID}},
	year = {2002},
	pages = {407--421}
},

@article{hong_source_2005,
	title = {Source density-driven independent component analysis approach for {fMRI} data},
	volume = {25},
	number = {3},
	journal = {Human brain mapping},
	author = {Hong, B. and Pearlson, {G.D.} and Calhoun, {V.D.}},
	year = {2005},
	pages = {297--307}
},

@article{ozaktas_fourier_1993,
	title = {Fourier transforms of fractional order and their optical interpretation},
	volume = {101},
	number = {3-4},
	journal = {Optics Communications},
	author = {Ozaktas, {H.M.} and Mendlovic, D.},
	year = {1993},
	pages = {163--169}
},

@article{pedersen_survey_????,
	title = {A {SURVEY} {OF} {CONVOLUTIVE} {BLIND} {SOURCE} {SEPARATION} {METHODS}},
	url = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.68.4632},
	journal = {{SPRINGER} {HANDBOOK} {ON} {SPEECH} {PROCESSING} {AND} {SPEECH} {COMMUNICATION}},
	author = {Pedersen, Michael Syskind and Larsen, Jan and Kjems, Ulrik and Parra, Lucas C}
},

@inproceedings{slock_spatio-temporal_1996,
	title = {Spatio-temporal training-sequence based channel equalization and adaptive interference cancellation},
	volume = {5},
	isbn = {0-7803-3192-3},
	doi = {10.1109/ICASSP.1996.550113},
	abstract = {We consider mobile radio communications with one user of interest and possibly interfering users and noise, over several discrete-time channels obtained either by oversampling or from multiple antennas. The optimal receiver structure for one signal of interest plus spatially and temporally correlated noise is {MLSE} equalization with an appropriately weighted metric for vector signals. We show however that we can alternatively pass the vector received signal through both a {MISO} (multi-input single output) matched filter and a {MIMO} blocking equalizer. The blocking equalizer output is independent of the signal of interest and is used as the input to a {MISO} Wiener filter that reduces the noise in the matched filter output. The training sequence of the signal of interest can be used to estimate the corresponding channel, from which the matched filter and blocking equalizer can be determined. The remaining quantities can be adapted from the available signals},
	booktitle = {, 1996 {IEEE} International Conference on Acoustics, Speech, and Signal Processing, 1996. {ICASSP-96.} Conference Proceedings},
	publisher = {{IEEE}},
	author = {Slock, D. {T.M}},
	month = may,
	year = {1996},
	keywords = {adaptive interference cancellation, adaptive signal processing, discrete time systems, discrete-time channels, equalisers, Equalizers, interference suppression, interfering users, Land mobile radio, matched filter, Matched filters, Maximum likelihood estimation, {MIMO}, {MIMO} blocking equalizer, {MIMO} systems, {MISO}, {MLSE} equalization, Mobile antennas, Mobile communication, mobile radio communications, multi-input single output, multiple antennas, noise, Noise reduction, optimal receiver structure, oversampling, radio receivers, radiofrequency interference, Receivers, spatio-temporal training-sequence based channel equalization, telecommunication channels, weighted metric, Wiener filter, Wiener filters},
	pages = {2714--2717 vol. 5}
},

@article{pierre_independent_1994,
	title = {Independent component analysis, A new concept?},
	volume = {36},
	issn = {0165-1684},
	url = {http://www.sciencedirect.com/science/article/pii/0165168494900299},
	doi = {10.1016/0165-1684(94)90029-9},
	abstract = {The independent component analysis {(ICA)} of a random vector consists of searching for a linear transformation that minimizes the statistical dependence between its components. In order to define suitable search criteria, the expansion of mutual information is utilized as a function of cumulants of increasing orders. An efficient algorithm is proposed, which allows the computation of the {ICA} of a data matrix within a polynomial time. The concept of {ICA} may actually be seen as an extension of the principal component analysis {(PCA)}, which can only impose independence up to the second order and, consequently, defines directions that are orthogonal. Potential applications of {ICA} include data analysis and compression, Bayesian detection, localization of sources, and blind identification and deconvolution.},
	number = {3},
	journal = {Signal Processing},
	author = {Pierre, Comon},
	month = apr,
	year = {1994},
	pages = {287--314}
},

@book{van_gerven_feedforward_1992,
	title = {Feedforward and feedback in a symmetric adaptive noise canceler : ``stability analysis in a simplified case''},
	shorttitle = {Feedforward and feedback in a symmetric adaptive noise canceler},
	url = {https://lirias.kuleuven.be/handle/123456789/169573},
	author = {Van Gerven, S. and Van Compernolle, Dirk},
	year = {1992},
	keywords = {{PSI\_SPEECH}}
},

@article{d_optical_1995,
	title = {Optical synthesis of {self-Fourier} functions},
	volume = {119},
	doi = {10.1016/0030-4018(95)00373-G},
	number = {3},
	journal = {Optics Communications},
	author = {D, Choudhury and {P.N}, Puntambekar and {A.K}, Chakraborty},
	year = {1995},
	pages = {279--282}
},

@article{zhang_geometrical_2002,
	title = {Geometrical structures of {FIR} manifold and multichannel blind deconvolution},
	volume = {31},
	number = {1},
	journal = {The Journal of {VLSI} Signal Processing},
	author = {Zhang, {L.Q.} and Cichocki, A. and Amari, S.},
	year = {2002},
	pages = {31--44}
},

@article{graps_introduction_1995,
	title = {An introduction to wavelets},
	volume = {2},
	number = {2},
	journal = {Computational Science \& Engineering, {IEEE}},
	author = {Graps, A.},
	year = {1995},
	pages = {50--61}
},

@techreport{mckeown_analysis_1997,
	title = {Analysis of {fMRI} data by blind separation into independent spatial components},
	institution = {{DTIC} Document},
	author = {{McKeown}, {M.J.}},
	year = {1997}
},

@article{choi_blind_2000-2,
	title = {Blind separation of nonstationary sources in noisy mixtures},
	volume = {36},
	number = {9},
	journal = {Electronics Letters},
	author = {Choi, S. and Cichocki, A.},
	year = {2000},
	pages = {848--849}
},

@article{molgedey_separation_1994,
	title = {Separation of a Mixture of Independent Signals Using Time Delayed Correlations},
	volume = {72},
	url = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.52.5680},
	journal = {{PHYSICAL} {REVIEW} {LETTERS}},
	author = {Molgedey, L. and Schuster, H. G},
	year = {1994},
	pages = {3634---3637}
},

@article{bastiaans_application_1997,
	title = {Application of the Wigner distribution function in optics},
	journal = {The Wigner Distribution Theory and Applications in Signal Processing},
	author = {Bastiaans, Martin J},
	year = {1997},
	pages = {1--54}
},

@article{makeig_independent_1996,
	title = {Independent component analysis of electroencephalographic data},
	journal = {Advances in neural information processing systems},
	author = {Makeig, S. and Bell, {A.J.} and Jung, {T.P.} and Sejnowski, {T.J.} and others},
	year = {1996},
	pages = {145--151}
},

@article{vigario_independent_1998-1,
	title = {Independent component analysis for identification of artifacts in magnetoencephalographic recordings},
	journal = {Advances in neural information processing systems},
	author = {Vig\'ario, R. and Jousm\"aki, V. and Haemaelaeninen, M. and Haft, R. and Oja, E.},
	year = {1998},
	pages = {229--235}
},

@article{ozaktas_fractional_1994,
	title = {Fractional Fourier transform as a tool for analyzing beam propagation and spherical mirror resonators},
	volume = {19},
	number = {21},
	journal = {Optics letters},
	author = {Ozaktas, {H.M.} and Mendlovic, D.},
	year = {1994},
	pages = {1678--1680}
},

@inproceedings{taleb_source_1998,
	title = {Source separation in post nonlinear mixtures: an entropy-based algorithm},
	volume = {4},
	shorttitle = {Source separation in post nonlinear mixtures},
	booktitle = {Acoustics, Speech and Signal Processing, 1998. Proceedings of the 1998 {IEEE} International Conference on},
	author = {Taleb, A. and Jutten, C. and Olympieff, S.},
	year = {1998},
	pages = {2089--2092}
},

@article{duarte_blind_2006,
	title = {Blind source separation of post-nonlinear mixtures using evolutionary computation and order statistics},
	journal = {Independent Component Analysis and Blind Signal Separation},
	author = {Duarte, L. and Suyama, R. and de Faissol Attux, R. and Von Zuben, F. and Romano, J.},
	year = {2006},
	pages = {66--73}
},

@inproceedings{choudrey_flexible_2001,
	title = {Flexible Bayesian independent component analysis for blind source separation},
	booktitle = {Proc. Int. Conf. on Independent Component Analysis and Signal Separation {(ICA2001)}},
	author = {Choudrey, {R.A.} and Roberts, {S.J.}},
	year = {2001},
	pages = {90--95}
},

@article{cardoso_infomax_1997,
	title = {Infomax and maximum likelihood for blind source separation},
	volume = {4},
	number = {4},
	journal = {Signal Processing Letters, {IEEE}},
	author = {Cardoso, {J.F.}},
	year = {1997},
	pages = {112--114}
},

@inproceedings{zhang_identifiability_2009,
	title = {On the identifiability of the post-nonlinear causal model},
	booktitle = {Proceedings of the {Twenty-Fifth} Conference on Uncertainty in Artificial Intelligence},
	author = {Zhang, K. and Hyv\"arinen, A.},
	year = {2009},
	pages = {647--655}
},

@inproceedings{yang_information_1997,
	title = {Information backpropagation for blind separation of sources in nonlinear mixture},
	volume = {4},
	booktitle = {Neural Networks, 1997., International Conference on},
	author = {Yang, {H.H.} and Amari, {S.I.} and Cichocki, A.},
	year = {1997},
	pages = {2141--2146}
},

@article{amari_novel_1997-1,
	title = {Novel On-line Adaptive Learning Algorithms for Blind Deconvolution using the Natural Gradient Approach},
	volume = {3},
	url = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.52.6203},
	journal = {{IN} {PROC.} {SYSID}, {KITAKYUSHU}},
	author = {Amari, Shun-ichi and Douglas, Scott C and Cichocki, Andrzej and Yang, Howard H},
	year = {1997},
	pages = {1057---1062}
},

@article{decorrelation_seungjin_????,
	title = {Seungjin {CHOI} and Andrzej {CICHOCKI} Lab for Artificial Brain Systems Frontier Research Program, {RIKEN} 2-1 Hirosawa, Wako-shi Saitama 351-01, Japan},
	author = {{DECORRELATION}, {S.T.}}
},

@article{cardoso_equivariant_1996,
	title = {Equivariant adaptive source separation},
	volume = {44},
	number = {12},
	journal = {Signal Processing, {IEEE} Transactions on},
	author = {Cardoso, {J.F.} and Laheld, {B.H.}},
	year = {1996},
	pages = {3017--3030}
},

@article{bastiaans_gabor?s_1994,
	title = {Gabor?s signal expansion and the Zak transform},
	volume = {33},
	shorttitle = {Gabor?},
	url = {http://ao.osa.org/abstract.cfm?URI=ao-33-23-5241},
	doi = {10.1364/AO.33.005241},
	abstract = {Gabor's expansion of a signal into a discrete set of shifted and modulated versions of an elementary signal is introduced, and its relation to sampling of the sliding-window spectrum is shown. It is shown how Gabor's expansion coefficients can be found as samples of the sliding-window spectrum, in which the window function is related to the elementary signal in such a way that the set of shifted and modulated elementary signals is biorthonormal to the corresponding set of window functions. The Zak transform is introduced, and its intimate relationship to Gabor's signal expansion is demonstrated. It is shown how the Zak transform can be helpful in determining the window function that corresponds to a given elementary signal and how it can be used to find Gabor's expansion coefficients. The continuous-time and the discrete-time cases are considered, and, by sampling the continuous frequency variable that still occurs in the discrete-time case, the discrete Zak transform and the discrete Gabor transform are introduced. It is shown how the discrete transforms enable us to determine Gabor's expansion coefficients by a fast computer algorithm, which is analogous to the well-known fast Fourier-transform= algorithm.},
	number = {23},
	journal = {Applied Optics},
	author = {Bastiaans, Martin J.},
	year = {1994},
	pages = {5241--5255}
},

@article{mendlovic_fractional_1993,
	title = {Fractional Fourier transforms and their optical implementation: I},
	volume = {10},
	shorttitle = {Fractional Fourier transforms and their optical implementation},
	number = {9},
	journal = {{JOSA} A},
	author = {Mendlovic, D. and Ozaktas, {H.M.}},
	year = {1993},
	pages = {1875--1881}
},

@article{jutten_blind_1991,
	title = {Blind separation of sources, part I: An adaptive algorithm based on neuromimetic architecture},
	volume = {24},
	shorttitle = {Blind separation of sources, part I},
	number = {1},
	journal = {Signal processing},
	author = {Jutten, C. and Herault, J.},
	year = {1991},
	pages = {1--10}
},

@inproceedings{cardoso_super-symmetric_1991,
	title = {Super-symmetric decomposition of the fourth-order cumulant tensor. Blind identification of more sources than sensors},
	booktitle = {Acoustics, Speech, and Signal Processing, 1991. {ICASSP-91.}, 1991 International Conference on},
	author = {Cardoso, {J.F.}},
	year = {1991},
	pages = {3109--3112}
},

@article{papy_exponential_2005,
	title = {Exponential data fitting using multilinear algebra: the single-channel and multi-channel case},
	volume = {12},
	shorttitle = {Exponential data fitting using multilinear algebra},
	number = {8},
	journal = {Numerical linear algebra with applications},
	author = {Papy, {J.M.} and De Lathauwer, L. and Van Huffel, S.},
	year = {2005},
	pages = {809--826}
},

@article{taleb_source_1999,
	title = {Source separation in post-nonlinear mixtures},
	volume = {47},
	number = {10},
	journal = {Signal Processing, {IEEE} Transactions on},
	author = {Taleb, A. and Jutten, C.},
	year = {1999},
	pages = {2807--2820}
},

@article{attias_independent_1999,
	title = {Independent factor analysis},
	volume = {11},
	number = {4},
	journal = {Neural computation},
	author = {Attias, H.},
	year = {1999},
	pages = {803--851}
},

@article{zhang_multichannel_1999,
	title = {Multichannel Blind Deconvolution of Non-minimum Phase Systems Using Information Backpropagation},
	volume = {52},
	url = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.15.4414},
	journal = {{IEEE} {TRANSACTIONS} {ON} {SIGNAL} {PROCESSING}},
	author = {Zhang, L. -q and Zhang, L. -q and Cichocki, A. and Cichocki, A. and Amari, S. and Amari, S.},
	year = {1999},
	pages = {210---216}
},

@article{vigario_independent_2000,
	title = {Independent component approach to the analysis of {EEG} and {MEG} recordings},
	volume = {47},
	number = {5},
	journal = {Biomedical Engineering, {IEEE} Transactions on},
	author = {Vig\'ario, R. and Sarela, J. and Jousmiki, V. and Hamalainen, M. and Oja, E.},
	year = {2000},
	pages = {589--593}
},

@article{tong_finite-step_1992,
	title = {A finite-step global convergence algorithm for the parameter estimation of multichannel {MA} processes},
	volume = {40},
	issn = {{1053587X}},
	url = {http://ieeexplore.ieee.org/Xplore/login.jsp?url=http%3A%2F%2Fieeexplore.ieee.org%2Fiel4%2F78%2F4076%2F00157295.pdf%3Farnumber%3D157295&authDecision=-203},
	doi = {10.1109/78.157295},
	number = {10},
	journal = {{IEEE} Transactions on Signal Processing},
	author = {Tong, L. and Inouye, Y. and Liu, R.-w.},
	month = oct,
	year = {1992},
	pages = {2547--2558}
},

@article{tong_waveform-preserving_1993,
	title = {Waveform-preserving blind estimation of multiple independent sources},
	volume = {41},
	number = {7},
	journal = {Signal Processing, {IEEE} Transactions on},
	author = {Tong, L. and Inouye, Y. and Liu, {R.W.}},
	year = {1993},
	pages = {2461--2470}
},

@article{antipolis_representations_2008,
	title = {Repr\'esentations parcimonieuses: de la S\'eparation de Sources au Compressed Sensing},
	shorttitle = {Repr\'esentations parcimonieuses},
	journal = {Sophia},
	author = {Antipolis, Sophia},
	year = {2008}
},

@article{bingham_fast_2000,
	title = {A fast fixed-point algorithm for independent component analysis of complex valued signals},
	volume = {10},
	number = {1},
	journal = {International Journal of Neural Systems},
	author = {Bingham, E. and Hyvarinen, A.},
	year = {2000},
	pages = {1--8}
},

@article{wang_stability_2006,
	title = {Stability analysis for stochastic {Cohen-Grossberg} neural networks with mixed time delays},
	volume = {17},
	number = {3},
	journal = {Neural Networks, {IEEE} Transactions on},
	author = {Wang, Z. and Liu, Y. and Li, M. and Liu, X.},
	year = {2006},
	pages = {814--820}
},

@article{salem_separation_2007,
	title = {S\'eparation aveugle de sources cyclostationnaires par utilisation des statistiques de second ordre},
	journal = {Majecstic},
	author = {Salem, Mohamed and Mohamed, Ould and Fenniri, Hassan and Delaunay, Georges},
	year = {2007},
	pages = {191--198}
},

@article{antonini_image_1992,
	title = {Image coding using wavelet transform},
	volume = {1},
	number = {2},
	journal = {Image Processing, {IEEE} Transactions on},
	author = {Antonini, M. and Barlaud, M. and Mathieu, P. and Daubechies, I.},
	year = {1992},
	pages = {205--220}
},

@article{wolf_wigner_1996,
	title = {Wigner distribution function for paraxial polychromatic optics},
	volume = {132},
	doi = {10.1016/0030-4018(96)00364-1},
	number = {3-4},
	journal = {Optics Communications},
	author = {Wolf},
	year = {1996},
	pages = {343--352}
},

@book{weinstein_multi-channel_1993-1,
	title = {Multi-channel signal separation},
	publisher = {Google Patents},
	author = {Weinstein, E. and Feder, M. and Oppenheim, {A.V.}},
	month = may,
	year = {1993},
	note = {{US} Patent 5,208,786}
},

@inproceedings{van_gerven_use_1994,
	title = {On the use of decorrelation in scalar signal separation},
	volume = {iii},
	isbn = {0-7803-1775-0},
	doi = {10.1109/ICASSP.1994.390091},
	abstract = {In this paper we describe a method to separate a scalar mixture of two signals. The method is based on the use of decorrelation as a signal separation criterion. It is proven analytically that decorrelating the output signals at different time lags is sufficient provided that the normalised autocorrelation functions of the source signals are sufficiently distinct. The method involves an iterative least-squares solution of a set of nonlinear equations. Alternatively, a gradient search algorithm can also be used to find the minimum of the sum of squares of these equations. Both time- and frequency-domain formulations are given. Some convergence and stability issues are discussed and a small example is given at the end},
	booktitle = {, 1994 {IEEE} International Conference on Acoustics, Speech, and Signal Processing, 1994. {ICASSP-94}},
	publisher = {{IEEE}},
	author = {Van Gerven, S. and Van Compernolle, D.},
	month = apr,
	year = {1994},
	keywords = {adaptive signal processing, convergence, convergence of numerical methods, decorrelation, Finite impulse response filter, frequency-domain analysis, frequency-domain formulations, gradient search algorithm, Iterative algorithms, iterative least-squares solution, iterative methods, least squares approximations, nonlinear equations, normalised autocorrelation functions, scalar signal separation, Signal analysis, Signal processing, Signal processing algorithms, Source separation, source signals, stability, time lags, time-domain analysis, time-domain formulations},
	pages = {III/57--III/60 vol.3}
},

@book{oja_independent_2001,
	title = {Independent component analysis},
	publisher = {John Wiley \& Sons},
	author = {Oja, E. and Hyvarinen, A. and Karhunen, J.},
	year = {2001}
},

@article{coffey_self-reciprocal_1994,
	title = {Self-reciprocal Fourier functions},
	volume = {11},
	url = {http://josaa.osa.org/abstract.cfm?URI=josaa-11-9-2453},
	doi = {10.1364/JOSAA.11.002453},
	abstract = {By definition, a self-reciprocal {(SR)} function is its own Fourier or Hankel transform. Areas of application of {SR} functions, including Fourier optics, are noted. Integral representations for {SR} functions are obtained and are illustrated with the exponential Fourier transformation on the half-line. It is pointed out that there are a large number of classes of {SR} functions, and examples of these functions are given.},
	number = {9},
	journal = {Journal of the Optical Society of America A},
	author = {Coffey, Mark W.},
	year = {1994},
	pages = {2453--2455}
},

@inproceedings{torkkola_blind_1996,
	title = {Blind separation of delayed sources based on information maximization},
	volume = {6},
	booktitle = {Acoustics, Speech, and Signal Processing, 1996. {ICASSP-96.} Conference Proceedings., 1996 {IEEE} International Conference on},
	author = {Torkkola, K.},
	year = {1996},
	pages = {3509--3512}
},

@inproceedings{bofill_blind_2000,
	title = {Blind separation of more sources than mixtures using sparsity of their short-time Fourier transform},
	booktitle = {Proc. {ICA2000}},
	author = {Bofill, P. and Zibulevsky, M.},
	year = {2000},
	pages = {87--92}
},

@article{ridgway_impact_1988,
	title = {The Impact of Array Detectors on High Resolution Infrared Spectroscopy},
	journal = {The Impact of Very High {S/N} Spectroscopy on Stellar Physics, G. Cayrel de Strobel and M. Spite, eds},
	author = {Ridgway, {S.T.} and Hinkle, {K.H.}},
	year = {1988},
	pages = {61--70}
},

@article{cardoso_blind_1998,
	title = {Blind signal separation: statistical principles},
	volume = {86},
	shorttitle = {Blind signal separation},
	number = {10},
	journal = {Proceedings of the {IEEE}},
	author = {Cardoso, {J.F.}},
	year = {1998},
	pages = {2009--2025}
},

@inproceedings{widrow_adaptive_1998,
	title = {Adaptive inverse control based on nonlinear adaptive filtering},
	booktitle = {Proceedings of 5th {IFAC} Workshop on Algorithms and Architectures for {Real-Time} Control {AARTC'98}},
	author = {Widrow, B. and Plett, {GL} and Ferreira, E. and Lamego, M.},
	year = {1998},
	pages = {247--252}
},

@article{murata_approach_2001,
	title = {An approach to blind source separation based on temporal structure of speech signals},
	volume = {41},
	number = {1},
	journal = {Neurocomputing},
	author = {Murata, N. and Ikeda, S. and Ziehe, A.},
	year = {2001},
	pages = {1--24}
},

@inproceedings{jutten_how_2007,
	title = {How to Apply {ICA} on Actual Data ? Example of Mars Hyperspectral Image Analysis},
	isbn = {1-4244-0882-2},
	shorttitle = {How to Apply {ICA} on Actual Data ?},
	doi = {10.1109/ICDSP.2007.4288502},
	abstract = {As any estimation method, results provided by {ICA} are dependent of a model - usually a linear mixture and separation model - and of a criterion - usually independence. In many actual problems, the model is a coarse approximation of the system physics and independence can be more or less satisfied, and consequently results are not reliable. Moreover, with many actual data, there is a lack of reliable knowledge on the sources to be extracted, and the interpretation of the independent components {(IC)} must be done very carefully, using partial prior information and with interactive discussions with experts. In this talk, we explain how such a scientific method can take place on the example of analysis of Mars hyperspectral images.},
	booktitle = {2007 15th International Conference on Digital Signal Processing},
	publisher = {{IEEE}},
	author = {Jutten, C. and Moussaoui, S. and Schmidt, F.},
	month = jul,
	year = {2007},
	keywords = {astronomical image processing, Bayesian source separation, Biomedical signal processing, hyperspectral image analysis, hyperspectral images, Hyperspectral imaging, Hyperspectral sensors, Image analysis, Independent component analysis, linear mixture, Mars, Mars Express, Mars hyperspectral images, Parametric statistics, Physics, positivity, separation model, Signal processing algorithms, Source separation},
	pages = {3--12}
},

@article{nuzillard_blind_2000,
	title = {Blind source separation and analysis of multispectral astronomical images},
	volume = {147},
	number = {1},
	journal = {Astronomy and Astrophysics Supplement Series},
	author = {Nuzillard, D. and Bijaoui, A.},
	year = {2000},
	pages = {129--138}
},

@book{cohen_time-frequency_1995,
	title = {Time-frequency analysis},
	isbn = {9780135945322},
	abstract = {Featuring traditional coverage as well as new research results that, until now, have been scattered throughout the professional literature, this book brings togetherin simple languagethe basic ideas and methods that have been developed to study natural and man-made signals whose frequency content changes with timee.g., speech, sonar and radar, optical images, mechanical vibrations, acoustic signals, biological/biomedical and geophysical {signals.Covers} time analysis, frequency analysis, and scale analysis; time-bandwidth relations; instantaneous frequency; densities and local quantities; the short time Fourier Transform; time-frequency analysis; the Wigner representation; time-frequency representations; computation methods; the synthesis problem; spatial-spatial/frequency representations; time-scale representations; operators; general joint representations; stochastic signals; and higher order time-frequency distributions. Illustrates each concept with examples and shows how the methods have been extended to other variables, such as {scale.For} engineers, acoustic scientists, medical scientists and developers, mathematicians, physicists, and mangers working in the fields of acoustics, sonar, radar, image processing, biomedical devices, communication.},
	publisher = {Prentice Hall {PTR}},
	author = {Cohen, L\'eon},
	year = {1995},
	keywords = {Frequency spectra, Mathematics / Probability \& Statistics / General, Signal processing, Technology \& Engineering / Electrical, Technology \& Engineering / Engineering {(General)}, Technology \& Engineering / Telecommunications, Time-series analysis}
},

@article{kreutz-delgado_dictionary_2003,
	title = {Dictionary learning algorithms for sparse representation},
	volume = {15},
	number = {2},
	journal = {Neural computation},
	author = {{Kreutz-Delgado}, K. and Murray, {J.F.} and Rao, {B.D.} and Engan, K. and Lee, {T.W.} and Sejnowski, {T.J.}},
	year = {2003},
	pages = {349--396}
},

@inproceedings{jutten_advances_2003,
	title = {Advances in nonlinear blind source separation},
	booktitle = {Proc. of the 4th Int. Symp. on Independent Component Analysis and Blind Signal Separation {(ICA2003)}},
	author = {Jutten, C. and Karhunen, J.},
	year = {2003},
	pages = {245--256}
},

@article{zhang_blind_2000,
	title = {Blind deconvolution of dynamical systems: A state space approach},
	volume = {4},
	shorttitle = {Blind deconvolution of dynamical systems},
	number = {2},
	journal = {Journal of Signal Processing},
	author = {Zhang, L. and Cichocki, A.},
	year = {2000},
	pages = {111--130}
},

@article{yang_information-theoretic_1998,
	title = {Information-theoretic approach to blind separation of sources in non-linear mixture},
	volume = {64},
	number = {3},
	journal = {Signal Processing},
	author = {Yang, {H.H.} and Amari, {S.I.} and Cichocki, A.},
	year = {1998},
	pages = {291--300}
},

@article{lee_blind_1999,
	title = {Blind source separation of more sources than mixtures using overcomplete representations},
	volume = {6},
	number = {4},
	journal = {Signal Processing Letters, {IEEE}},
	author = {Lee, {T.W.} and Lewicki, {M.S.} and Girolami, M. and Sejnowski, {T.J.}},
	year = {1999},
	pages = {87--90}
},

@article{sidiropoulos_parallel_2000,
	title = {Parallel factor analysis in sensor array processing},
	volume = {48},
	number = {8},
	journal = {Signal Processing, {IEEE} Transactions on},
	author = {Sidiropoulos, {N.D.} and Bro, R. and Giannakis, {G.B.}},
	year = {2000},
	pages = {2377--2388}
},

@inproceedings{amari_multichannel_1997,
	title = {Multichannel blind deconvolution and equalization using the natural gradient},
	booktitle = {Signal Processing Advances in Wireless Communications, 1997 First {IEEE} Signal Processing Workshop on},
	author = {Amari, S. and Douglas, {S.C.} and Cichocki, A. and Yang, {H.H.}},
	year = {1997},
	pages = {101--104}
},

@article{vorobyov_blind_2002,
	title = {Blind noise reduction for multisensory signals using {ICA} and subspace filtering, with application to {EEG} analysis},
	volume = {86},
	number = {4},
	journal = {Biological cybernetics},
	author = {Vorobyov, S. and Cichocki, A.},
	year = {2002},
	pages = {293--303}
},

@inproceedings{salam_blind_2001,
	title = {Blind source recovery: algorithms for static and dynamic environments},
	volume = {2},
	shorttitle = {Blind source recovery},
	booktitle = {Neural Networks, 2001. Proceedings. {IJCNN'01.} International Joint Conference on},
	author = {Salam, {F.M.} and Erten, G. and Waheed, K.},
	year = {2001},
	pages = {902--907}
},

@article{oja_nonlinear_1997,
	title = {The nonlinear {PCA} learning rule in independent component analysis},
	volume = {17},
	number = {1},
	journal = {Neurocomputing},
	author = {Oja, E.},
	year = {1997},
	pages = {25--45}
},

@book{cichocki_blind_2002,
	title = {Blind Signal and Image Processing},
	publisher = {Wiley Online Library},
	author = {Cichocki, A. and Amari, S.},
	year = {2002}
},

@book{golub_matrix_1996,
	edition = {third edition},
	title = {Matrix Computations},
	isbn = {0801854148},
	publisher = {The Johns Hopkins University Press},
	author = {Golub, Gene H. and Loan, Charles F. van Van},
	month = oct,
	year = {1996}
},

@inproceedings{zhang_multichannel_1999-1,
	title = {Multichannel blind deconvolution of non-minimum phase systems using information backpropagation},
	volume = {1},
	booktitle = {Neural Information Processing, 1999. Proceedings. {ICONIP'99.} 6th International Conference on},
	author = {Zhang, {L.Q.} and Cichocki, A. and Amari, S.},
	year = {1999},
	pages = {210--216}
},

@article{gelle_blind_2000,
	title = {Blind sources separation applied to rotating machines monitoring by acoustical and vibrations analysis},
	volume = {14},
	number = {3},
	journal = {Mechanical Systems and Signal Processing},
	author = {Gelle, G. and Colas, M. and Delaunay, G.},
	year = {2000},
	pages = {427--442}
},

@inproceedings{zhang_kalman_2000,
	title = {Kalman filter and state-space approach to blind deconvolution},
	volume = {1},
	booktitle = {Neural Networks for Signal Processing X, 2000. Proceedings of the 2000 {IEEE} Signal Processing Society Workshop},
	author = {Zhang, {L.Q.} and Cichocki, A. and Amari, S.},
	year = {2000},
	pages = {425--434}
},

@article{amari_statistical_1993,
	title = {Statistical theory of learning curves under entropic loss criterion},
	volume = {5},
	number = {1},
	journal = {Neural Computation},
	author = {Amari, {S.I.} and Murata, N.},
	year = {1993},
	pages = {140--153}
},

@article{calhoun_semi-blind_2005,
	title = {Semi-blind {ICA} of {fMRI:} A method for utilizing hypothesis-derived time courses in a spatial {ICA} analysis},
	volume = {25},
	shorttitle = {Semi-blind {ICA} of {fMRI}},
	number = {2},
	journal = {Neuroimage},
	author = {Calhoun, {VD} and Adali, T. and Stevens, {MC} and Kiehl, {KA} and Pekar, {JJ}},
	year = {2005},
	pages = {527--538}
},

@article{pham_blind_1997,
	title = {Blind separation of mixture of independent sources through a quasi-maximum likelihood approach},
	volume = {45},
	number = {7},
	journal = {Signal Processing, {IEEE} Transactions on},
	author = {Pham, {D.T.} and Garat, P.},
	year = {1997},
	pages = {1712--1725}
},

@article{cichocki_robust_1994,
	title = {Robust learning algorithm for blind separation of signals},
	volume = {30},
	number = {17},
	journal = {Electronics letters},
	author = {Cichocki, A. and Unbehauen, R. and Rummert, E.},
	year = {1994},
	pages = {1386--1387}
},

@inproceedings{waheed_blind_2003,
	title = {Blind multi user detection in {DS-CDMA} systems using natural gradient based symbol recovery structures},
	booktitle = {Proc. 4th Int. Conf. Independent Component Analysis and Blind Signal Separation, Nara, Japan},
	author = {Waheed, K. and Desai, K. and Salem, {F.M.}},
	year = {2003}
},

@article{li_underdetermined_2006,
	title = {Underdetermined blind source separation based on sparse representation},
	volume = {54},
	number = {2},
	journal = {Signal Processing, {IEEE} Transactions on},
	author = {Li, Y. and Amari, {S.I.} and Cichocki, A. and Ho, {D.W.C.} and Xie, S.},
	year = {2006},
	pages = {423--437}
},

@article{anemuller_complex_2003,
	title = {Complex independent component analysis of frequency-domain electroencephalographic data},
	volume = {16},
	number = {9},
	journal = {Neural Networks},
	author = {Anem\"uller, J. and Sejnowski, {T.J.} and Makeig, S.},
	year = {2003},
	pages = {1311--1323}
},

@article{le_bihan_singular_2004,
	title = {Singular value decomposition of quaternion matrices: a new tool for vector-sensor signal processing},
	volume = {84},
	shorttitle = {Singular value decomposition of quaternion matrices},
	number = {7},
	journal = {Signal Processing},
	author = {Le Bihan, N. and Mars, J.},
	year = {2004},
	pages = {1177--1199}
},

@article{karhunen_class_1997,
	title = {A class of neural networks for independent component analysis},
	volume = {8},
	number = {3},
	journal = {Neural Networks, {IEEE} Transactions on},
	author = {Karhunen, J. and Oja, E. and Wang, L. and Vigario, R. and Joutsensalo, J.},
	year = {1997},
	keywords = {Artificial neural networks, basis vector estimation, Blind source separation, feedforward neural nets, Higher order statistics, image processing, Independent component analysis, multilayer feedforward networks, Neural networks, neural structures, Principal component analysis, Robustness, Signal processing, Signal processing algorithms, statistical analysis, Unsupervised learning, Vectors},
	pages = {486--504}
},

@article{vasilescu_multilinear_2002,
	title = {Multilinear analysis of image ensembles: Tensorfaces},
	shorttitle = {Multilinear analysis of image ensembles},
	journal = {Computer {Vision---ECCV} 2002},
	author = {Vasilescu, M. and Terzopoulos, D.},
	year = {2002},
	pages = {447--460}
},

@inproceedings{taleb_nonlinear_1997,
	title = {Nonlinear source separation: The post-nonlinear mixtures},
	shorttitle = {Nonlinear source separation},
	booktitle = {European symposium on artificial neural networks},
	author = {Taleb, A. and Jutten, C.},
	year = {1997},
	pages = {279--284}
},

@book{te-won_independent_1998,
	title = {Independent component analysis, theory and applications},
	publisher = {Boston: Kluwer Academic Publishers},
	author = {{Te-Won}, L.},
	year = {1998}
},

@article{calhoun_unmixing_2006,
	title = {Unmixing {fMRI} with independent component analysis},
	volume = {25},
	number = {2},
	journal = {Engineering in Medicine and Biology Magazine, {IEEE}},
	author = {Calhoun, {V.D.} and Adali, T.},
	year = {2006},
	pages = {79--90}
},

@inproceedings{calhoun_ica_2003,
	title = {{ICA} of functional {MRI} data: an overview},
	shorttitle = {{ICA} of functional {MRI} data},
	booktitle = {in Proceedings of the International Workshop on Independent Component Analysis and Blind Signal Separation},
	author = {Calhoun, {V.D.} and Adali, T. and Hansen, {L.K.} and Larsen, J. and Pekar, {J.J.}},
	year = {2003}
},

@inproceedings{torkkola_blind_1999,
	title = {Blind separation for audio signals--are we there yet},
	booktitle = {Proc. Int. Workshop on Independent Component Analysis and Blind Separation of Signals {(ICA'99)}},
	author = {Torkkola, K.},
	year = {1999},
	pages = {239--244}
},

@article{cohen_time-frequency_1989,
	title = {Time-frequency distributions-a review},
	volume = {77},
	issn = {0018-9219},
	doi = {10.1109/5.30749},
	abstract = {A review and tutorial of the fundamental ideas and methods of joint time-frequency distributions is presented. The objective of the field is to describe how the spectral content of a signal changes in time and to develop the physical and mathematical ideas needed to understand what a time-varying spectrum is. The basic gal is to devise a distribution that represents the energy or intensity of a signal simultaneously in time and frequency. Although the basic notions have been developing steadily over the last 40 years, there have recently been significant advances. This review is intended to be understandable to the nonspecialist with emphasis on the diversity of concepts and motivations that have gone into the formation of the field},
	number = {7},
	journal = {Proceedings of the {IEEE}},
	author = {Cohen, L.},
	month = jul,
	year = {1989},
	keywords = {Astronomy, Cities and towns, diversity of concepts, Fourier transforms, fundamental ideas, joint time-frequency distributions, motivations, Physics, review, reviews, Signal analysis, Signal processing, signal resolution, spectral analysis, Spectrogram, Speech analysis, Strips, Time frequency analysis, time-varying spectrum, tutorial},
	pages = {941--981}
},

@inproceedings{wang_crosscorrelation_1996,
	title = {Crosscorrelation estimation using teacher forcing Hebbian learning and its application},
	volume = {1},
	booktitle = {Neural Networks, 1996., {IEEE} International Conference on},
	author = {Wang, C. and Wu, {H.C.} and Principe, {JC}},
	year = {1996},
	pages = {282--287}
},

@article{feichtinger_gabor_1992,
	title = {Gabor wavelets and the Heisenberg group: Gabor expansions and short time Fourier transform from the group theoretical point of view},
	shorttitle = {Gabor wavelets and the Heisenberg group},
	author = {Feichtinger, {H.G.} and Gr\"ochenig, K.},
	year = {1992}
},

@article{achard_identifiability_2005,
	title = {Identifiability of post-nonlinear mixtures},
	volume = {12},
	number = {5},
	journal = {Signal Processing Letters, {IEEE}},
	author = {Achard, S. and Jutten, C.},
	year = {2005},
	pages = {423--426}
},

@techreport{porrill_independent_1997,
	title = {Independent components analysis for signal separation and dimension reduction},
	institution = {Citeseer},
	author = {Porrill, J. and Stone, J.},
	year = {1997}
},

@article{mansour_adaptive_2000,
	title = {Adaptive subspace algorithm for blind separation of independent sources in convolutive mixture},
	volume = {48},
	number = {2},
	journal = {Signal Processing, {IEEE} Transactions on},
	author = {Mansour, A. and Jutten, C. and Loubaton, P.},
	year = {2000},
	pages = {583--586}
},

@article{calhoun_independent_2002,
	title = {Independent component analysis of {fMRI} data in the complex domain},
	volume = {48},
	number = {1},
	journal = {Magnetic Resonance in Medicine},
	author = {Calhoun, {VD} and Adal\i{}, T. and Pearlson, {GD} and Van Zijl, {PCM} and Pekar, {JJ}},
	year = {2002},
	pages = {180--192}
},

@inproceedings{cardoso_iterative_1992,
	title = {Iterative techniques for blind source separation using only fourth-order cumulants},
	volume = {92},
	booktitle = {Proc. {EUSIPCO}},
	author = {Cardoso, {J.F.}},
	year = {1992},
	pages = {739--742}
},

@inproceedings{parra_convolutive_1998,
	title = {Convolutive blind source separation based on multiple decorrelation},
	booktitle = {Neural Networks for Signal Processing {VIII}, 1998. Proceedings of the 1998 {IEEE} Signal Processing Society Workshop},
	author = {Parra, L. and Spence, C. and De Vries, B.},
	year = {1998},
	pages = {23--32}
},

@inproceedings{eriksson_blind_2002,
	title = {Blind identifiability of class of nonlinear instantaneous {ICA} models},
	volume = {2},
	booktitle = {Proc. of the {XI} European Signal Proc. {Conf.(EUSIPCO} 2002},
	author = {Eriksson, J. and Koivunen, V.},
	year = {2002},
	pages = {7--10}
},

@article{jung_removing_2000,
	title = {Removing electroencephalographic artifacts by blind source separation},
	volume = {37},
	number = {02},
	journal = {Psychophysiology},
	author = {Jung, {T.P.} and Makeig, S. and Humphries, C. and Lee, {T.W.} and Mckeown, {M.J.} and Iragui, V. and Sejnowski, {T.J.}},
	year = {2000},
	pages = {163--178}
},

@inproceedings{achard_blind_2001,
	title = {Blind source separation in post nonlinear mixtures},
	booktitle = {Proc. Int. Workshop on Independent Component Analysis and Blind Signal Separation {(ICA2001)}},
	author = {Achard, S. and Pham, {D.T.} and Jutten, C.},
	year = {2001},
	pages = {295--300}
},

@article{hyvarinen_fast_1997,
	title = {A fast fixed-point algorithm for independent component analysis},
	volume = {9},
	number = {7},
	journal = {Neural computation},
	author = {Hyv\"arinen, A. and Oja, E.},
	year = {1997},
	pages = {1483--1492}
},

@article{li_global_1996,
	title = {Global convergence of fractionally spaced Godard {(CMA)} adaptive equalizers},
	volume = {44},
	number = {4},
	journal = {Signal Processing, {IEEE} Transactions on},
	author = {Li, Y. and Ding, Z.},
	year = {1996},
	pages = {818--826}
},

@inproceedings{akay_wavelets_1997,
	title = {Wavelets for biomedical signal processing},
	volume = {6},
	booktitle = {Engineering in Medicine and Biology Society, 1997. Proceedings of the 19th Annual International Conference of the {IEEE}},
	author = {Akay, M. and Mello, C.},
	year = {1997},
	pages = {2688--2691}
},

@article{cardoso_component_2008,
	title = {Component separation with flexible {models---Application} to multichannel astrophysical observations},
	volume = {2},
	number = {5},
	journal = {Selected Topics in Signal Processing, {IEEE} Journal of},
	author = {Cardoso, {J.F.} and Le Jeune, M. and Delabrouille, J. and Betoule, M. and Patanchon, G.},
	year = {2008},
	pages = {735--746}
},

@article{biswal_blind_1999,
	title = {Blind source separation of multiple signal sources of {fMRI} data sets using independent component analysis},
	volume = {23},
	number = {2},
	journal = {Journal of computer assisted tomography},
	author = {Biswal, {B.B.} and Ulmer, {J.L.}},
	year = {1999},
	pages = {265}
},

@article{gardner_new_1991,
	title = {A new method of channel identification},
	volume = {39},
	issn = {0090-6778},
	doi = {10.1109/26.87168},
	abstract = {A method of channel identification is proposed that exploits the spectral correlation properties of pulse- and carrier-modulated signals to identify channels in the presence of arbitrary noise and nearly arbitrary interference. Although a pilot or training signal is required, no replica of the transmitted pilot/training signal is needed at the receiver. The price paid for this simplicity and the tolerance to extreme channel corruption from noise or interference is that the method is slow. That is, relatively long averaging times are needed for measurement of the spectral correlation of the received signal},
	number = {6},
	journal = {{IEEE} Transactions on Communications},
	author = {Gardner, W. A},
	month = jun,
	year = {1991},
	keywords = {Adaptive equalizers, averaging times, carrier-modulated signals, channel corruption, channel identification, Data communication, Digital modulation, Distortion measurement, Interference, noise, pilot signal, pulse modulated signals, Pulse modulation, Pulse shaping methods, received signal, Shape, Signal processing, spectral analysis, spectral correlation measurement, spectral correlation properties, telecommunication channels, training signal, Transfer functions},
	pages = {813--817}
},

@article{karhunen_neural_1997,
	title = {On neural blind separation with noise suppression and redundancy reduction},
	volume = {8},
	number = {2},
	journal = {International Journal of Neural Systems},
	author = {Karhunen, J. and Cichocki, A. and Kasprzak, W. and Pajunen, P.},
	year = {1997},
	pages = {219--238}
},

@article{li_analysis_2004,
	title = {Analysis of sparse representation and blind source separation},
	volume = {16},
	number = {6},
	journal = {Neural computation},
	author = {Li, Y. and Cichocki, A. and Amari, S.},
	year = {2004},
	pages = {1193--1234}
},

@article{mallat_matching_1993,
	title = {Matching pursuits with time-frequency dictionaries},
	volume = {41},
	number = {12},
	journal = {Signal Processing, {IEEE} Transactions on},
	author = {Mallat, {S.G.} and Zhang, Z.},
	year = {1993},
	pages = {3397--3415}
},

@article{yang_adaptive_1997,
	title = {Adaptive online learning algorithms for blind separation: maximum entropy and minimum mutual information},
	volume = {9},
	shorttitle = {Adaptive online learning algorithms for blind separation},
	number = {7},
	journal = {Neural computation},
	author = {Yang, {H.H.} and Amari, S.},
	year = {1997},
	pages = {1457--1482}
},

@article{hyvarinen_independent_2000,
	title = {Independent component analysis: algorithms and applications},
	volume = {13},
	shorttitle = {Independent component analysis},
	number = {4-5},
	journal = {Neural networks},
	author = {Hyv\"arinen, A. and Oja, E.},
	year = {2000},
	pages = {411--430}
},

@book{ding_blind_2001,
	title = {Blind equalization and identification},
	isbn = {9780824704797},
	abstract = {This text seeks to clarify various contradictory claims regarding capabilities and limitations of blind equalization. It highlights basic operating conditions and potential for malfunction. The authors also address concepts and principles of blind algorithms for single input multiple output {(SIMO)} systems and multi-user extensions of {SIMO} equalization and identification.},
	publisher = {Marcel Dekker},
	author = {Ding, Zhi and Li, Geoffrey},
	month = jan,
	year = {2001},
	keywords = {Digital communications, Medical / General, Signal processing, Signal processing - Digital techniques, Signal processing/ Digital techniques, Technology \& Engineering / Electrical, Technology \& Engineering / Electronics / Digital, Technology \& Engineering / Engineering {(General)}, Technology \& Engineering / Telecommunications}
},

@article{hinton_fast_2006,
	title = {A fast learning algorithm for deep belief nets},
	volume = {18},
	number = {7},
	journal = {Neural computation},
	author = {Hinton, {G.E.} and Osindero, S. and Teh, {Y.W.}},
	year = {2006},
	pages = {1527--1554}
},

@article{chi_blind_2002,
	title = {Blind {MAI} and {ISI} suppression for {DS/CDMA} systems using {HOS-based} inverse filter criteria},
	volume = {50},
	number = {6},
	journal = {Signal Processing, {IEEE} Transactions on},
	author = {Chi, {C.Y.} and Chen, {C.H.} and Chen, {C.Y.}},
	year = {2002},
	pages = {1368--1381}
},

@book{kagan_characterization_1973,
	title = {Characterization Problems in Mathematical Statistics},
	isbn = {0471454214},
	publisher = {John Wiley \& Sons Inc},
	author = {Kagan, {A.M.} and etc and Linnik, {IuriiVladimirovich} and Rao, C. Radhakrishna},
	translator = {Ramachandran, Balasubrahmanyan},
	month = dec,
	year = {1973}
},

@inproceedings{choi_blind_2001,
	title = {Blind separation of second-order nonstationary and temporally colored sources},
	booktitle = {Statistical Signal Processing, 2001. Proceedings of the 11th {IEEE} Signal Processing Workshop on},
	author = {Choi, S. and Cichocki, A. and Belouchrani, A.},
	year = {2001},
	pages = {444--447}
},

@article{liu_independent_2003,
	title = {Independent component analysis of Gabor features for face recognition},
	volume = {14},
	number = {4},
	journal = {Neural Networks, {IEEE} Transactions on},
	author = {Liu, C. and Wechsler, H.},
	year = {2003},
	pages = {919--928}
},

@article{hlawatsch_linear_1992,
	title = {Linear and quadratic time-frequency signal representations},
	volume = {9},
	issn = {1053-5888},
	doi = {10.1109/79.127284},
	abstract = {A tutorial review of both linear and quadratic representations is given. The linear representations discussed are the short-time Fourier transform and the wavelet transform. The discussion of quadratic representations concentrates on the Wigner distribution, the ambiguity function, smoothed versions of the Wigner distribution, and various classes of quadratic time-frequency representations. Examples of the application of these representations to typical problems encountered in time-varying signal processing are provided.{\textbackslash}textless{\textbackslash}textgreater},
	number = {2},
	journal = {{IEEE} Signal Processing Magazine},
	author = {Hlawatsch, F. and {Boudreaux-Bartels}, G. F},
	month = apr,
	year = {1992},
	keywords = {ambiguity function, Fourier transforms, Frequency domain analysis, frequency-domain analysis, linear representations, quadratic representations, reviews, short-time Fourier transform, Signal analysis, Signal processing, Signal representations, signal resolution, Signal synthesis, Time domain analysis, Time frequency analysis, time-domain analysis, time-frequency signal representations, time-varying signal processing, transforms, tutorial review, wavelet transform, Wavelet transforms, Wigner distribution},
	pages = {21--67}
},

@article{delorme_enhanced_2007,
	title = {Enhanced detection of artifacts in {EEG} data using higher-order statistics and independent component analysis},
	volume = {34},
	number = {4},
	journal = {Neuroimage},
	author = {Delorme, A. and Sejnowski, T. and Makeig, S.},
	year = {2007},
	pages = {1443--1449}
},

@article{shi_new_2004,
	title = {A new fixed-point algorithm for independent component analysis},
	volume = {56},
	journal = {Neurocomputing},
	author = {Shi, Z. and Tang, H. and Tang, Y.},
	year = {2004},
	pages = {467--473}
},

@inproceedings{torkkola_blind_????,
	title = {Blind separation of convolved sources based on information maximization},
	isbn = {0-7803-3550-3},
	url = {http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=548372},
	doi = {10.1109/NNSP.1996.548372},
	publisher = {{IEEE}},
	author = {Torkkola, K.},
	pages = {423--432}
},

@article{makeig_mining_2004,
	title = {Mining event-related brain dynamics},
	volume = {8},
	number = {5},
	journal = {Trends in cognitive sciences},
	author = {Makeig, S. and Debener, S. and Onton, J. and Delorme, A.},
	year = {2004},
	pages = {204--210}
},

@article{alieva_self-imaging_1999,
	title = {Self-imaging in first-order optical systems},
	volume = {1},
	journal = {Optics and Optoelectronics: Theory, Devices and Applications, {OP} Nijhawan, {AK} Gupta, {AK} Musla, and K. Singh, {eds.(Narosa}, New Delhi, 1998)},
	author = {Alieva, T. and Bastiaans, {M.J.}},
	year = {1999},
	pages = {126--131}
}